www.interviewkickstart.com Open in urlscan Pro
52.17.119.105 Public Scan

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Submitted URL:
http://www.interviewkickstart.com/
Effective URL:
https://www.interviewkickstart.com/
Submission: On January 09 via api (January 9th 2024, 1:20:30 am UTC) from US — Scanned from DE

Form analysis
6 forms found in the DOM

Name: wf-form-Webinar-Registration-Part-1 — GET

<form id="wf-form-Webinar-Registration-Part-1" name="wf-form-Webinar-Registration-Part-1" data-name="Webinar Registration Part 1" method="get" class="webinar__registration-form1" data-wf-page-id="653b9c0abfa9142eb0846d44"
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    <input type="hidden" value="bb26089b-d5c7-4c9f-8a67-32128dd703fa-1704763225475" name="User ID" class="user_id">
    <input type="hidden" value="ik.com/" name="CTA Page" class="cta_page_url">
    <input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
    <input type="hidden" value="How to Nail your next Technical Interview" name="Event Name">
    <input type="hidden" value="Europe/Berlin" name="user_timezone" class="user_timezone">
    <input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
    <input type="hidden" value="" name="site_url" class="site_url">
    <input type="hidden" value="Germany" name="country" class="v_country">
    <input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
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    <input type="hidden" value="No" name="Is it exit intent popup?" class="is_exit_intent_popup">
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  <div class="webinar__registration-step1">
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      <div class="step__card active">
        <div class="steps__number">
          <div>1</div>
        </div>
        <div>Enter details</div>
      </div>
      <div class="arrow-lcon"></div>
      <div class="step__card">
        <div class="steps__number">
          <div>2</div>
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        <div>Select webinar slot</div>
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    </div>
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      <div id="w-node-_2a5c101b-ea03-3bc9-e707-a7a615756e4f-15756e2d" class="webinar_field-block">
        <div class="form-label-row"><label for="First-Name" class="webinar_filed-text">First Name</label>
          <div class="form-invalid-msg first-name-error hide">*Invalid Name</div>
        </div><input class="webinar_field-input first-name w-input" autocomplete="on" maxlength="256" name="First-Name" data-name="First Name" placeholder="Enter first name" type="text" id="First-Name">
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      <div id="w-node-_1e1724a1-f571-362a-1d27-81e2d16d3a11-15756e2d" class="webinar_field-block">
        <div class="form-label-row"><label id="Last-Name-2" for="First-Name" class="webinar_filed-text">Last Name</label>
          <div class="form-invalid-msg last-name-error hide">*Invalid Name</div>
        </div><input class="webinar_field-input last-name w-input" maxlength="256" name="Last-Name" data-name="Last Name" placeholder="Enter last name" type="text" id="Last-Name">
      </div>
    </div>
    <div class="webinar_field-block">
      <div class="form-label-row"><label id="Email-Address" for="Email-Address-3" class="webinar_filed-text">Email Address</label>
        <div class="form-invalid-msg email-id-error hide">*Invalid Email Address</div>
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    </div><input type="submit" data-wait="Please wait..." class="bc__btn-select-webinar-slot-v2 w-button" value="Select your webinar time">
    <div class="webinar-lighbox-small-text">By sharing your contact details, you agree to our <a href="/privacy-policy" class="link_privacy_policy">privacy policy.</a></div>
  </div>
</form>

Name: wf-form-Webinar-Registration-Part-2 — GET

<form id="Webinar-Registration-Part-2" name="wf-form-Webinar-Registration-Part-2" data-name="Webinar Registration Part 2" method="get" class="webinar__registration-form2" data-wf-page-id="653b9c0abfa9142eb0846d44"
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      <div class="step__card completed">
        <div class="steps__number completed">
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        <div>Enter details</div>
      </div>
      <div class="arrow-lcon"></div>
      <div class="step__card active">
        <div class="steps__number">
          <div>2</div>
        </div>
        <div>Select webinar slot</div>
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    </div>
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      <input type="hidden" value="" name="Last Name" class="wr__lastname">
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      <input type="hidden" value="" name="Phone Number" class="wr__phone">
      <input type="hidden" value="" name="webinar-type" class="webinar-type">
      <input type="hidden" value="" name="Webinar Lead Type" class="webinar-lead-type">
      <input type="hidden" value="" name="City" class="wr__city">
      <input type="hidden" value="Desktop" name="Device" class="wr__device">
      <input type="hidden" value="" name="Region" class="wr__region">
      <input type="hidden" value="" name="Referrer" class="wr__referrer">
      <input type="hidden" value="" name="Event Start Time" class="wr__event-start-time">
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      <input type="hidden" value="" name="Invitee Start Time" class="wr__invitee-start-time">
      <input type="hidden" value="" name="Invitee End Time" class="wr__invitee-end-time">
      <input type="hidden" value="How to Nail your next Technical Interview" name="Event Name">
      <input type="hidden" value="bb26089b-d5c7-4c9f-8a67-32128dd703fa-1704763225475" name="User ID" class="user_id">
      <input type="hidden" value="ik.com/" name="CTA Page" class="cta_page_url">
      <input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
      <input type="hidden" value="Organic" name="utm_source" class="utm_source">
      <input type="hidden" value="" name="utm_medium" class="utm_medium">
      <input type="hidden" value="" name="utm_campaign" class="utm_campaign">
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      <input type="hidden" value="" name="utm_adset" class="utm_adset">
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      <input type="hidden" value="" name="msclkid" class="msclkid">
      <input type="hidden" value="" name="fbclid" class="fbclid">
      <input type="hidden" value="Europe/Berlin" name="user_timezone" class="user_timezone">
      <input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
      <input type="hidden" value="" name="site_url" class="site_url">
      <input type="hidden" value="Germany" name="country" class="v_country">
      <input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
      <input type="hidden" value="" name="phone_number_full" class="tno1">
      <input type="hidden" value="No" name="Is it exit intent popup?" class="is_exit_intent_popup">
    </div>
    <div class="webinar__slots"><label class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-10T02:30:00+01:00" data-endtime="2024-01-10T03:30:00+01:00" data-invitee_starttime="02:30AM - Wednesday, January 10, 2024"
          data-invitee_endtime="03:30AM - Wednesday, January 10, 2024" data-name="2024-01-10T02:30:00+01:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="SWITCH_UP"><span class="w-form-label"
          for="start-date-0">Wednesday, 10 January 2024 | 02:30 AM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-11T01:30:00+01:00" data-endtime="2024-01-11T02:30:00+01:00"
          data-invitee_starttime="01:30AM - Thursday, January 11, 2024" data-invitee_endtime="02:30AM - Thursday, January 11, 2024" data-name="2024-01-11T01:30:00+01:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input"
          data-webinar_lead_type="SWITCH_UP"><span class="w-form-label" for="start-date-1">Thursday, 11 January 2024 | 01:30 AM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date"
          value="2024-01-12T02:30:00+01:00" data-endtime="2024-01-12T03:30:00+01:00" data-invitee_starttime="02:30AM - Friday, January 12, 2024" data-invitee_endtime="03:30AM - Friday, January 12, 2024" data-name="2024-01-12T02:30:00+01:00"
          class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="SWITCH_UP"><span class="w-form-label" for="start-date-2">Friday, 12 January 2024 | 02:30 AM</span></label><label
        class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-13T01:30:00+01:00" data-endtime="2024-01-13T02:30:00+01:00" data-invitee_starttime="01:30AM - Saturday, January 13, 2024"
          data-invitee_endtime="02:30AM - Saturday, January 13, 2024" data-name="2024-01-13T01:30:00+01:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="SWITCH_UP"><span class="w-form-label"
          for="start-date-3">Saturday, 13 January 2024 | 01:30 AM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date" value="2024-01-15T02:30:00+01:00" data-endtime="2024-01-15T03:30:00+01:00"
          data-invitee_starttime="02:30AM - Monday, January 15, 2024" data-invitee_endtime="03:30AM - Monday, January 15, 2024" data-name="2024-01-15T02:30:00+01:00" class="w-form-formradioinput select-webinar-radio-btn w-radio-input"
          data-webinar_lead_type="SWITCH_UP"><span class="w-form-label" for="start-date-4">Monday, 15 January 2024 | 02:30 AM</span></label><label class="select-webinar-slot w-radio"><input type="radio" name="start-date"
          value="2024-01-16T01:30:00+01:00" data-endtime="2024-01-16T02:30:00+01:00" data-invitee_starttime="01:30AM - Tuesday, January 16, 2024" data-invitee_endtime="02:30AM - Tuesday, January 16, 2024" data-name="2024-01-16T01:30:00+01:00"
          class="w-form-formradioinput select-webinar-radio-btn w-radio-input" data-webinar_lead_type="SWITCH_UP"><span class="w-form-label" for="start-date-5">Tuesday, 16 January 2024 | 01:30 AM</span></label></div>
    <div class="timezone-disclaimer">*All webinar slots are in the <span class="var_localtimezone">US/Pacific</span> timezone</div>
    <div class="second-btn_block"><input type="submit" data-wait="Please wait..." class="bc__btn-2nd-step w-button" value="Finish"><a href="#" class="btn-back-to-step1">Back</a></div>
  </div>
</form>

Name: wf-form-Coding-Experience-Experiment — GET

<form id="wf-form-Coding-Experience-Experiment" name="wf-form-Coding-Experience-Experiment" data-name="Coding Experience Experiment" method="get" class="coding-experience-experiment" data-wf-page-id="653b9c0abfa9142eb0846d44"
  data-wf-element-id="61c4c4c3-9c75-8c5f-4bc7-e316001590dd" aria-label="Coding Experience Experiment">
  <div class="w-embed"><input type="hidden" value="Organic" name="utm_source" class="utm_source">
    <input type="hidden" value="" name="utm_medium" class="utm_medium">
    <input type="hidden" value="" name="utm_campaign" class="utm_campaign">
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    <input type="hidden" value="ik.com/" name="CTA Page" class="cta_page_url">
    <input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
    <input type="hidden" value="Europe/Berlin" name="user_timezone" class="user_timezone">
    <input type="hidden" value="https://www.interviewkickstart.com/" name="page_url" class="page_url">
    <input type="hidden" value="" name="site_url" class="site_url">
    <input type="hidden" value="Germany" name="country" class="v_country">
    <input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
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      style="opacity:0;position:absolute;z-index:-1" value="No coding experience/Currently studying"><span class="radio-button-label-2 w-form-label" for="No-coding-experience-Currently-studying">No coding experience/Currently studying</span>
  </label><label class="form-radio w-radio">
    <div class="w-form-formradioinput w-form-formradioinput--inputType-custom exp-custom-radio w-radio-input"></div><input id="0-4-years-2" type="radio" name="years-of-experience" data-name="years-of-experience"
      style="opacity:0;position:absolute;z-index:-1" value="0-4 years"><span class="radio-button-label-2 w-form-label" for="0-4-years-2">0-4 years</span>
  </label><label class="form-radio w-radio">
    <div class="w-form-formradioinput w-form-formradioinput--inputType-custom exp-custom-radio w-radio-input"></div><input id="4-years" type="radio" name="years-of-experience" data-name="years-of-experience"
      style="opacity:0;position:absolute;z-index:-1" value="4+ years"><span class="radio-button-label-2 w-form-label" for="4-years">4+ years</span>
  </label>
</form>

Name: wf-form-Exit-Intent-Free-Course-Offering — GET

<form id="wf-form-Exit-Intent-Free-Course-Offering" name="wf-form-Exit-Intent-Free-Course-Offering" data-name="Exit Intent Free Course Offering" method="get" class="exit-intent-free-course-form" data-wf-page-id="653b9c0abfa9142eb0846d44"
  data-wf-element-id="ccbb98f2-4ba5-ca7b-756b-f63678e9d0ae" aria-label="Exit Intent Free Course Offering">
  <div>
    <h2 class="bc-popup-title t-green-2 text-green tale-green _4"><strong>FREE course on 'Sorting Algorithms'</strong> by <span class="text-span-2"><strong>Omkar Deshpande <em class="omkar-designation">(Stanford PhD, Head of Curriculum,
            IK)</em></strong></span></h2>
    <div class="w-embed"><input type="hidden" value="Organic" name="utm_source" class="utm_source">
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      <input type="hidden" value="ik.com" name="Landing Page" class="l_page_url">
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      <input type="hidden" value="Germany" name="country" class="v_country">
      <input type="hidden" value="" name="salesforce_uuid" class="salesforce_uuid">
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  </div>
  <div class="form-fields-row"><input class="form-input-field mb-0 exitintent-fc-email w-input" maxlength="256" name="Email-Address" data-name="Email Address" placeholder="Enter your email id" type="email" id="Email-Address-6" required=""><input
      type="submit" data-wait="Please wait..." class="btn-getaccess w-button" value="Get Access Now"></div>
</form>

Name: wf-form-Download-Course-Brochure---Organic-LPs — GET

<form id="wf-form-Download-Course-Brochure---Organic-LPs" name="wf-form-Download-Course-Brochure---Organic-LPs" data-name="Download Course Brochure - Organic LPs" method="get" class="form__download-brochure" data-wf-page-id="653b9c0abfa9142eb0846d44"
  data-wf-element-id="cf66683e-86b7-31b5-3aff-2e9f8e17b603" aria-label="Download Course Brochure - Organic LPs">
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    <input type="hidden" class="page_url" id="page_url" name="page_url" value="https://www.interviewkickstart.com/">
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    <input type="hidden" class="v_country" id="v_country" name="v_country" value="Germany">
    <input type="hidden" class="" id="" name="Course" value="Machine Learning / AI">
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  <div class="form__step1">
    <h3 class="form-card__title">Download the Machine Learning<br>Course Brochure</h3>
    <p class="form-card__sub-text">Get all the details about the course &amp; pricing.</p>
    <div class="w-layout-grid grid">
      <div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b60d-b0846d44"><label for="First-Name-9" class="form-label-1">First name <span class="fname-error hide">*required</span></label><input class="form-input first-name w-input" maxlength="256"
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          name="Last-Name-2" data-name="Last Name 2" placeholder="Last name" type="text" id="Last-Name-9"></div>
      <div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b619-b0846d44"><label for="Phone-Number" class="form-label-1">Phone number <span class="mobile-error hide">*required</span></label><input class="form-input phone w-input" maxlength="256"
          name="Phone-Number" data-name="Phone Number" placeholder="Enter contact number" type="text" id="Phone-Number"></div>
      <div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b61f-b0846d44"><label for="Email-Address-7" class="form-label-1">Email address <span class="email-error hide">*required</span></label><input class="form-input email w-input" maxlength="256"
          name="Email-Address-7" data-name="Email Address 7" placeholder="Enter email address" type="text" id="Email-Address-7">
        <div class="spacer _32"></div><label class="w-checkbox checkbox-field mr-20"><input id="checkbox-2" type="checkbox" name="checkbox-2" data-name="Checkbox 2" class="w-checkbox-input" checked=""><span class="check-box-text w-form-label"
            for="checkbox-2">By providing your contact information you agree to our <a href="#" target="_blank">Privacy Policy</a></span></label>
        <div class="text-center"><a data-cta-id="download_course_brochure_step_1" href="#" class="form-cta btn-download-brochure w-button">Download Brochure</a>
          <div class="spacer-3 _16"></div>
        </div>
      </div>
    </div>
  </div>
  <div class="form__step2">
    <h3 class="form-card__title">Almost there...</h3>
    <div class="spacer _20"></div>
    <div class="w-layout-grid grid">
      <div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b635-b0846d44"><label for="Work-Experience" class="form-label-1">Your work experience (in years)</label><select id="Work-Experience" name="Work-Experience" data-name="Work Experience"
          class="form-select-2 w-select">
          <option value="">Select one...</option>
          <option value="0-2">0-2</option>
          <option value="3-8">3-8</option>
          <option value="9-15">9-15</option>
          <option value="16-20">16-20</option>
          <option value="20+">20+</option>
        </select></div>
      <div id="w-node-cf66683e-86b7-31b5-3aff-2e9f8e17b639-b0846d44"><label for="Domain-Role" class="form-label-1">Your domain/role</label><select id="Domain-Role" name="Domain-Role" data-name="Domain Role" class="form-select-2 w-select">
          <option value="">Select one...</option>
          <option value="Back-end">Back-end</option>
          <option value="Cloud Engineer">Cloud Engineer</option>
          <option value="Cyber Security">Cyber Security</option>
          <option value="Data Engineer">Data Engineer</option>
          <option value="Data Science">Data Science</option>
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HOW TO NAIL YOUR NEXT TECHNICAL INTERVIEW

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Wednesday, 10 January 2024 | 02:30 AMThursday, 11 January 2024 | 01:30 AMFriday,
12 January 2024 | 02:30 AMSaturday, 13 January 2024 | 01:30 AMMonday, 15 January
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YOU MAY BE MISSING OUT ON A 66.5% SALARY HIKE*

I want my career success nowI want to delay my success



NICK CAMILLERI

Head of Career Skills Development & Coaching
*Based on past data of successful IK students
Help us know you better!


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HOW CAN WE HELP?

Interview Kickstart has enabled over 3500 engineers to uplevel.




REGISTER FOR WEBINAR

Our founder takes you through how to Nail Complex Technical Interviews.




READ OUR REVIEWS

Our alumni credit the Interview Kickstart programs for their success.




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One of our Program Advisors will get back to you ASAP.

USA's #1 Tech Interview Prep Platform is now in India!
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About usWhy usInstructorsReviewsCostFAQContactBlogRegister for Webinar
Accelerate Your Tech Career: Select the best path for you
Interview Prep for top tech companies
Switch to Machine Learning/Data Science


NAIL YOUR
NEXT TECHNICAL
INTERVIEW

... by becoming a better engineer

Learn how to boost your interview success rate

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WHY CHOOSE INTERVIEW KICKSTART?

Here's the TL;DR version


LIVE CLASSES BY OVER 500 FAANG+ HIRING MANAGERS

$312K AVERAGE SALARY

FOR SDES, EMS, TPMS, PMS, DATA ANALYSTS


SALARY HIKES OF $100K - $150K

8 YRS IN BUSINESS, THOUSANDS OF FAANG+ OFFERS

100% MONEYBACK GUARANTEE



Register for webinar
It's Free


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Top companies love hiring our candidates

Top companies hiring now




DON’T WAIT!

January 2024
COHORTS
GOING FAST

December 2023
COHORTS
ENROLLMENTS CLOSED

PRIOR COHORTS

OVERSUBSCRIBED



500+

Instructors, Coaches & Interviewers
from Top Tech Companies


17,000+

Students



18


Highest number of offers
received by an IK alum


$1.2M

Highest compensation received
by our Alum


$312,275


Average value of offers
received by alums


66.5%


Avg. salary hike for alums
who upleveled


INSTRUCTORS AND MENTORS FROM FAANG & TIER-1 COMPANIES




YUVAL SCHARF


Software Engineer



ADRIAN
FERNANDEZ


Engineering Manager



QIUPING X


Principal Scientist Manager



ZHUANG
LIANG


Director Of Engineering



OMKAR DESHPANDE


Head of Technical Curriculum

Slide 4 of 5.





YUVAL SCHARF


Software Engineer



ADRIAN
FERNANDEZ


Engineering Manager



QIUPING XU


Principal Scientist Manager



ZHUANG
LIANG


Director Of Engineering



NICK CAMILLERI


Head of Career Skills Development and Coaching



OMKAR DESHPANDE


Head of Technical Curriculum

Meet your instructors


PICK A PROGRAM THAT SUITS YOUR GOAL




STEPUP

Accelerated interview prep to step up into a Tier-1 company


< 2 Months to prepare
Self-paced course
10 mentor sessions/mock interviews
Customizable
Placement assistance
Unlimited coaching sessions
Visa advice
Learn more


LEVELUP

Guided interview prep to level up into a Tier-1 company


3+ Months to prepare
POPULAR
Instructor-led live course
15-21 mentor sessions/mock interviews
Customizable
Placement assistance
Unlimited coaching sessions
Visa advice
Learn more


SWITCHUP

Upskill and switch to a new role at a Tier-1 tech company


11+ Months to prepare
Instructor-led live course
15 mentor sessions/mock interviews
Available for Data Science and ML Engineers
Placement assistance
Unlimited coaching sessions
Visa advice
Choose domain
Become an AI/ML Data ScientistBecome an AI/ML Engineer


18 LEVELUP COURSES FOR KEY TECH ROLES

Includes domain training, coding, systems design, behavioral interview prep,
mock interviews & lifelong learning.

Back-end EngineeringFull Stack EngineeringFront-end EngineeringEngineering
ManagerEarly EngineeringEmbedded SystemsMachine LearningData EngineeringSite
Reliability Engineering
iOS EngineeringAndroid EngineeringTest EngineeringTechnical Program ManagerData
ScienceProduct Manager (Tech)Security EngineeringAWS Cloud Solutions
ArchitectData Analyst & Business Analyst

Not sure which course to select?

No problem - you can change your course anytime during the first 3 weeks


SOFTWARE COURSES


Back-end Engineering
Full Stack Engineering
Front-end Engineering
Test Engineering
iOS Engineering
Android Engineering
Early Engineering


TECH MANAGEMENT COURSES


Engineering Manager
Technical Program Manager
Product Manager (Tech)


DATA COURSES


Machine Learning
Data Engineering
Data Science
Data Analyst & Business Analyst


SYSTEMS COURSES


Embedded Systems
AWS Cloud Solutions Architect
Site Reliability Engineering
Cyber Security

Not sure which course to select?
No problem - you can change your course anytime during the first 3 weeks

To learn more about the Courses

Register for our Webinar
Register for our Webinar


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INTERVIEWING IS A SKILL

Top tech companies receive thousands of applications. To identify the best
candidates, their interviews are designed to be extremely challenging.

You
 * Are expected to prepare for the interview.
 * Are tested on core DSA, system design, and behavioral skills, in addition to
   your domain.
 * Have to come up with solutions to complex problems in a short span of time.
 * Are asked to articulate your assumptions, your decisions and your solutions.


IK PREP - DESIGNED FOR SUCCESS

COMPREHENSIVE
CURRICULUM

DSA, System Design, Domain Concepts, Career Skills, and more

RIGOROUS MOCK
INTERVIEWS

With actual Hiring Managers.
Get Detailed Feedback, Scores and Reference Videos

PLENTY OF
1 X 1 HELP

Technical Coaching, Homework Assistance, Solutions Discussion and Individual
sessions

PERSONALIZED
FEEDBACK

Brutally honest and structured - the kind that actually helps

CAREER SKILLS
DEVELOPMENT

Resume Help, LinkedIn Profile Feedback, Personal Branding and Live Workshops

SALARY
NEGOTIATION

Company-, role-, and level-specific help based on real data from instructors and
students


DESIGNED FOR WORKING PROFESSIONALS

EVENINGS & WEEKENDS

Intense, but designed to fit into your work and life schedule.

REMOTE

Participate in live classes remotely.

LONG SUPPORT PERIOD

To help you catch up with everything that we offer.



GET ACCESS TO UPLEVEL WHEN YOU ENROLL IN A COURSE

In-browser online judge, mock interview suite, on-demand timed tests, and more
to add structure to your interview prep journey

Learn more


TOP OFFERS FROM THE BEST COMPANIES

From Entry Level to Directorial Level


... and many more.


TESTIMONIALS

Our alums talk about how IK helped them succeed

Each instructor-led session was packed with information and there were lots of
problems to practice. The course was intense, but it was a great use of my time.

Neelesh Tendulkar
Offers
Google, Intuit
Interview Kickstart is like a fitness coach which guides to achieve your dream
job. It can help you identify your weak points and also suggest steps to improve
them.

Swapnil Tailor
Offers
Facebook, Twitter, Linkedin
The classes, workshops, quizzes, practice problems, and mock interviews provided
me with the knowledge, tools, and the feedback that I was missing. Interview
Kickstart showed me how to prepare for success.

Flavia Vela
Offers
LinkedIn, Amazon
I can't think of a better recipe for tech interview success than combining the
Interview Kickstart program with hard work. The program made my prep much more
effective and eliminated surprises from the interview process.

Michael Huston
Offers
Databricks, Amazon, Airbnb
IK provides a nice, structured way to prepare for interviews while having a
full-time job. Mock interviews helped me get better and the problem sets
alleviated the need for me to source problems externally.

Kushal L
Offers
Facebook
“The course was very intense. During the two months it lasted, I would easily
work 2+ hours every day, weekends included, on the homework problems. This
course is just practice, practice, practice. And it works! Fast forward a couple
of weeks, and I accepted my offer with Facebook.”

Davide Testuggine
Offers
Facebook




Read more reviews


OUR METHODOLOGY WORKS

Our courses constantly evolve based on industry trends, instructor insights, and
feedback from students

SOLVE UNSEEN PROBLEMS

Recognize patterns in interview problems and rehearse them until you feel
prepared.

OVERCOME INTERVIEW ANXIETY

You're good at what you do, but anxiety kills your interviews. Get over it with
prolific practice.

50% MONEY-BACK GUARANTEE*

If you do well in our StepUp and LevelUp programs but still don't land a
domain-relevant job within the post-program support period, we'll refund 50% of
the tuition you paid for the course.*


READY TO
ENROLL?

Get your enrollment process started by registering for a Pre-enrollment Webinar
with one of our Founders.


Register for our Webinar
Register for our Webinar


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ABOUT US

Interview Kickstart, established in 2014, is the gold standard for Technical
Interview Preparation. Our 500+ instructors, drawn from tech giants like Google
and Amazon, have guided 17,000+ engineers beyond skill enhancement- from mock
interviews and real-world projects to effective salary negotiation. The outcome?
Alumni landing jobs with $300K+ offers, and the highest compensation at a
whopping $1.28 Million.

Switchup: TRANSFORM YOUR CAREER


TRANSITION TO AI/ MACHINE LEARNING ENGINEERING ROLES AT TIER-1 COMPANIES

4.65
Students enrolled: 240

Designed and taught by FAANG+ AI/Machine Learning Engineers to help you
transform your career and land your dream job.

ML Engineers!
Get interview-ready with lessons from FAANG+ experts
Master core Machine Learning concepts
Sharpen your coding and system design skills
Machine Learning
Register for webinar
Learn more about the course & pricing
It's Free


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Course Overview
Start Learning
Get all the information about the course and pricing in our live webinar with
Q&A.

Download Course Brochure
Almost full
Next Batch
12th June, 2022
Location
Live & online
Duration
4 months (apx. 10 hours/week)

CurriculumMeet the instructorsTypical Week at IKMock interviewsInterview Prep
GuideCareer Impact
CurriculumMeet the instructorsTypical Week at IKMock interviewsInterview Prep
GuideCareer Impact


STUDENTS WHO CHOSE TO UPLEVEL WITH IK GOT PLACED AT

Siva Karthik Gade
SDE, Machine Learning

Sai Marapa Reddy
SWE, Machine Learning

Safir Merchant
SWE, Machine Learning

Jameson Merkow
Principal AI Engineer

Sayan Banerjee
Data Scientist II

Manika Kapoor
Senior Deep Learning Scientist

Mike Kane
Lead Data Engineer, Analytics

Akshay Lodha
Data Engineering & Analytics

Anju Mercian
Data Engineering Consultant

Alokkumar Roy
Data Engineer


17,000+
Tech professionals trained
$1.2M
Highest offer received by an IK alum
53%
Average salary hike received by alums



AI/MACHINE LEARNING COURSE CURRICULUM


PART 1: MASTERING MACHINE LEARNING

Foundations
2 months
5 live classes

1


PYTHON FUNDAMENTALS

 * Variables, if-else, loops, Functions, lists, strings, etc. related coding
   examples
 * Tuples, Set, Dict, map, filter, reduce etc. related coding examples
 * OOP in Python, File and Exception handling
 * Numpy, Pandas, etc.
 * Visualisation with Python

2


SOFTWARE DEVELOPMENT ESSENTIALS

 * Scripting, Git & GitHub
 * Client-Server Architecture, HTTP & REST APIs
 * Databases & Intro to SQL

Essential Mathematics for Machine Learning
2 months
3 live classes

1


ESSENTIALS OF PROBABILITY

2


PROBABILITY DISTRIBUTION

3


ESSENTIALS OF STATISTICS

4


HYPOTHESIS TESTING

5


BASIC AND LINEAR ALGEBRA

6


CALCULUS

7


VECTORS AND MATRICES

8


REGRESSION

Deep Dive into Machine Learning Engineering
6 months
5 live classes

1


FOUNDATIONAL MACHINE LEARNING CONCEPTS

 * Data Pipelines for ML: Techniques and Best Practices
 * Supervised Machine Learning Techniques and Applications
 * Unsupervised Machine Learning Techniques and Applications
 * Introduction to Neural Networks and Deep Learning Architectures such as RNN,
   LSTM, CNN etc
 * AI Development through Multiple Mini Projects: Hands-on Techniques and
   Applications

2


ADVANCED MACHINE LEARNING FRAMEWORK: TECHNIQUES AND BEST PRACTICES FOR
SUCCESSFUL DEVELOPMENT


 * NLP: Techniques and Applications of Natural Language Processing using
   Embeddings, Autoencoders, VAE, GANs etc.
 * Generative AI : BERT, Transformers, and LLMs for Advanced AI Development
 * Computer Vision Techniques and Applications in AI for Image and Video
   Analysis : Object Detection, Boundary Detection, Image Segmentation etc
 * Reinforcement Learning through Human Feedback (RLHF) : Introduction and
   Applications in Generative AI.
 * Deep Learning Mini Projects: Hands-on Techniques and Applications to build a
   system from scratch.

3


ML DEVELOPMENT AND DEPLOYMENT: ADVANCED ML AND MLOPS TECHNIQUES


 * Software System Design Fundamentals: Principles and Best Practices for
   AI-based Development
 * ML Design Principles: Guiding principles for developing effective ML systems.
 * Scoping ML Projects: Defining goals, objectives, and boundaries of ML
   projects
 * Distributed Training: Data & Model Parallelism using GPUs
 * Model Deployment: Best Practices for Successful Implementation and
   Maintenance at Scale
 * Improving Model Performance: Techniques and Strategies for Retraining and
   Model Decay
 * AI Model Monitoring and Maintenance: Best Practices for Ensuring Optimal
   Performance
 * Failure Analysis: Techniques and Strategies for Diagnosing Production Issues
 * Ensuring ML Model Stability: Techniques and Best Practices

4


CAPSTONE PROJECT: REAL-WORLD APPLICATIONS AND HANDS-ON EXPERIENCE IN MACHINE
LEARNING


 * Industry-Relevant AI Projects: Techniques and Best Practices for Developing
   Real-world Solutions
 * AI Mentorship at FAANG Companies: Techniques and Best Practices for Career
   Development
 * Capstone Presentation: Best Practices for Presenting Machine Learning
   Solutions to Stakeholders

Understanding the ML Development Framework
3 weeks
3 live classes

1


BASICS OF SOFTWARE SYSTEM DESIGN

2


ML DESIGN PRINCIPLES


3


ML PROJECT SCOPING


4


ML MODEL DEPLOYMENT


Advanced Machine Learning and MLOPs
5 weeks

1


ADVANCED MACHINE LEARNING

 * Recommendation Systems
 * Natural Language Processing
 * Modern ML Architectures

2


MLOPS


 * Model performance and re-training
 * Model Monitoring
 * Diagnosing Production Failures
 * Model Stability

Capstone Project
2 weeks

1


INDUSTRY-RELEVANT PROJECTS, REPLICATING REAL-LIFE PROJECTS AT TIER-1 COMPANIES




PART 2: INTERVIEW PREPARATION

Data Structures and Algorithms Interview Preparation
4 weeks
5 live classes

1


TREES

2


GRAPHS

3


GREEDY ALGORITHMS

4


DYNAMIC PROGRAMMING

Software System Design Interview Preparation
3 weeks
5 live classes

1


ONLINE PROCESSING SYSTEMS

2


BATCH PROCESSING SYSTEMS

3


STREAM PROCESSING SYSTEMS AND OBJECT MODELING

AI/Machine Learning Interview Preparation
5 weeks
5 live classes

1


SUPERVISED LEARNING I - RANK RELEVANT SEARCH RESULTS

2


SUPERVISED LEARNING II - DESIGN A YOUTUBE VIDEO RECOMMENDATION SYSTEM

3


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MATT NICKENS

Manager, Data Science
10+ years experience

Learn more

Dipan has valuable work experience of over 10 years with companies such as
SpaceX.
As an operations-focused engineer, he has worked with cross-functional teams
such as design, test, production, and supply chain to accomplish mission
objectives.As an operations-focused engineer, he has worked with
cross-functional teams such as design, test, production, and supply chain to
accomplish mission objectives.


JAMESON MERKOW

Principal AI Engineer
11+ years experience
Learn more

Dipan has valuable work experience of over 10 years with companies such as
SpaceX.
As an operations-focused engineer, he has worked with cross-functional teams
such as design, test, production, and supply chain to accomplish mission
objectives.As an operations-focused engineer, he has worked with
cross-functional teams such as design, test, production, and supply chain to
accomplish mission objectives.


CHRISTIAN MONSON

Machine Learning Scientist
9+ years experience
Learn more

Dipan has valuable work experience of over 10 years with companies such as
SpaceX.
As an operations-focused engineer, he has worked with cross-functional teams
such as design, test, production, and supply chain to accomplish mission
objectives.As an operations-focused engineer, he has worked with
cross-functional teams such as design, test, production, and supply chain to
accomplish mission objectives.


ALIREZA DIRAFZOON

Research Engineer
7+ years experience
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Dipan has valuable work experience of over 10 years with companies such as
SpaceX.
As an operations-focused engineer, he has worked with cross-functional teams
such as design, test, production, and supply chain to accomplish mission
objectives.As an operations-focused engineer, he has worked with
cross-functional teams such as design, test, production, and supply chain to
accomplish mission objectives.




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SIVA KARTHIK GADE

SDE — Machine Learning
Placed at:
IK offers high-quality study material, knowledgeable and patient instructors
working at industry-leading companies, well-paced live classes + tests + review
sessions, always available technical + career coaches, mock interview support
from the best interviewers in the respective fields. IK brings together people
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NADHA GAFUR

Machine Learning Engineer
Placed at:
It has been a great learning experience. The structure is really good and the
materials as well. The lectures and live class pre-reading material is very
informative and engaging.


SAI MARAPA REDDY

SWE, Machine Learning
Placed at:
I completed IK’s program and got offers from a couple of FAANG companies. Why
you should take this course: It is well tested and the focus is more on the
concepts/templates rather than approaching one problem at a time. You will meet
peers who have similar aspirations. You can make groups and help yourselves.


JAMESON MERKOW

Principal AI Engineer
Placed at:
I joined IK because I had a lot of really terrible experiences with interviews.
The confidence and expertise I routinely demonstrated in the workplace was not
translating to interviews. I lacked confidence during behavioral interviews and
felt completely lost when asked  coding questions. IK taught me how to clearly
demonstrate my skills and experience during interviews which ultimately helped
me find a Principal engineering position at Microsoft.


SAFIR MERCHANT

Machine Learning Software Engineer
Placed at:
I liked the course that IK provided a lot. IK provided all the knowledge on a
variety of topics that helped me prepare for coding interviews. The mock
interviews were really great. Landing a job at my desired company has been a
great pleasure.


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A FREE GUIDE TO KICKSTART YOUR MACHINE LEARNING CAREER AT FAANG+

From the interview process and career path to interview questions and salary
details — learn everything you need to know about Machine Learning careers at
top tech companies.
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Interview Strategy and Success
Interview Questions
Career Path
Salary and Levels at FAANG
Frequently asked questions


MACHINE LEARNING INTERVIEW PROCESS OUTLINE

Typically, the Machine Learning interview process at FAANG+ and other Tier-1
companies include the following rounds:
Initial technical screening
 * Basic ML understanding, including a discussion on past ML projects
 * Coding problems

3-8 on-site rounds
 * 1-2 coding rounds:
 * Coding problems on Data Structures and Algorithms
 * ML algorithm coding + project discussion
 * 1-2 system design rounds:
 * Scalable/software design
 * ML Systems
 * 1-3 ML technical rounds (ML breadth and depth understanding):
 * ML Algorithms
 * Deployment Tools and techniques

Behavioral round
 * Open-ended questions to gauge if you're a "good fit” for the company

What to Expect at Machine Learning Engineer Interviews
1
Initial phone/technical screening round:
This can be a combination of basic ML understanding round/past projects or
purely coding-based: Medium Hard LC questions. Some companies refuse to move
forward if you fail the initial ML screen. 
2
3-8 On-site rounds: 
 * Coding round: This can be a mix of project discussion and coding
 * System design round: Mix of questions on how to design a general software
   system
 * ML system design round  (1-2 rounds): For example, design a recommendation
   system for Netflix. For candidates having less than 3 years of experience, ML
   system design is often replaced by another core ML Understanding Round of
   medium to high difficulty
 * ML fundamental round: Familiarity with algorithms such as Linear/Logistic
   Regression, Decision Trees, SVM, Deep Neural Networks and optimization
   techniques, loss functions such as Gradient Descent, Cross-Entropy Loss, etc.
   These questions can vary based on the specific role and team you are applying
   for
 * Behavioral round: You can expect questions on your job experience and
   discussions on past projects along with open-ended questions to understand if
   you’re a good fit for the role.

For more specific information on the Machine Learning Engineer’s interview
process at FAANG+ companies, check out:
 * Machine Learning Engineering Interviews
 * Google Machine Learning Engineer Interview Process
 * Amazon Machine Learning Engineer Interview Process
 * Apple Machine Learning Engineer Interview Prep

3

Interview Process for Different Machine Learning-Related Roles 
A typical ML Engineer interview consists of:
1-2 coding rounds – Usually, Data Structures and Algorithms based questions are
asked, but some companies also ask you to code basic ML algorithms (Usually in
Python)
1-2 system design rounds – One general system design round (like SDE profile)
and another ML System design round
1 behavioral round — Questions regarding your past work experience will be asked
to see if you’re a cultural fit
1-2 ML fundamentals rounds: These can cover areas such as: 
 * Discussion on past projects in a related field 
 * Understanding of various ML algorithms and their underlying principles
 * Discussion on challenges and tradeoffs related to each algorithm


A typical Applied Scientist interview consists of:
1 coding round – Usually includes questions on Data Structures and Algorithms,
but some companies ask to code basic ML algorithms (Python)
1 ML system design round – Mainly focused on ML understanding (compared with the
MLE round, where model production and deployment are equally important), i.e.,
identifying a suitable dataset for the problem, feature engineering, tradeoffs,
sampling, etc. 
1-2 ML Depth and Breadth rounds: Deep dive into ML fundamentals about their
prior experience
1 behavioral round — Questions regarding your past work experience will be asked
to see if you’re a cultural fit.

A typical Research Scientist interview consists of:
1 coding round – Usually Python library-based (Pytorch/Tensorflow) or LeetCode
Easy in some companies. 
1 ML problem-solving round – Identifying a suitable dataset for the problem,
feature engineering, tradeoffs, experimentation design, how to establish a
baseline, modifying current algorithms to suit the situation, etc.
1 presentation round – Present some research problem (from the Ph.D. thesis,
previous work experience, or any new topic relevant to the interviewing team),
followed by QnAs. Expected to have a firm grasp of Concepts and Advancements in
the given problem to answer applied questions.
1-2 ML Depth and Breadth rounds – Deep dive into ML fundamentals about their
prior experience. Expected to have proficiency in ML Algorithms from the
mathematical to the application level.  
1 behavioral round — Questions regarding your past work experience will be asked
to see if you’re a cultural fit.

For more information on the interview process, read our blog on Machine Learning
Engineering Interviews.


MACHINE LEARNING INTERVIEW QUESTIONS

The interview process for the various Machine Learning positions is quite
rigorous, so you need to be prepared accordingly. To get you started, we've
compiled a list of the most frequently asked Machine Learning interview
questions and segmented them into different categories.
1

Machine Learning Interview Questions on Coding
You are given some corrupted text with all the spaces removed. Implement an
algorithm to recover the original text.

Given a sorted integer array, find the index of a given number’s first or last
occurrence. If the element is not present in the array, report that as well.

Given: Two strings, A and B, of the same length n. Find: Whether it’s possible
to cut both strings at a common point such that the first part of A and the
second part of B form a palindrome.

Given a tree, write a function to return the sum of the max-sum path which goes
through the root node.

Given an infinite chessboard, find the shortest distance for a knight to move
from position A to position B.

Implement a k-means clustering algorithm with just NumPy and Python built-ins.

Given a filter and an image, implement a convolution. Follow up with a given
stride length, padding, etc.

2

Machine Learning Interview Questions on System Design
Design an application for inventory data management.

Write a program to retrieve log data in an optimal way.

How would you design a function that schedules jobs on a rack of machines
knowing that each job requires a certain amount of CPU & RAM, and each machine
has different amounts of CPU & RAM?

Design a “Hey Siri” style trigger word detection system.

In-flight entertainment systems have a vast library of movies that users can
enjoy during their journey. Design a system that recommends a set of movies to
watch based on the user's preferences and total flight time.

How would you detect fraud or predatory house listings on Airbnb?

3

Machine Learning interview Questions on ML Basics
Does the vanishing gradient problem occur closer to the beginning or end of the
neural network training process?

Explain why XGBoost performs better than SVM.

How do you deal with imbalanced data?

When using sci kit-learn, do we need to scale our feature values when they vary
greatly?

How would you select the value of "k" in a k-means algorithm?

What is the difference between the normal, soft-margin SVM and SVM with a linear
kernel?

How would you detect spam emails? What is the best metric for this type of
system: precision or recall?

What do you mean by a generative model?

Which methods can you use to summarize the content of 1000 tweets? 

What are the different ways of preventing over-fitting in a deep neural network?
Explain the intuition behind each.

4

 Open-ended Machine Learning Interview Questions
According to you, which is the most valuable data in our business?

What are your thoughts on our current data process?

How can we use your Machine Learning skills to generate revenue?

How will you quantify the level of success of the projects you implement?

Pick any product or app that you really like and describe how you would improve
it.

For more such questions, read 50+ Machine Learning Interview Questions and
Advanced Machine Learning Interview Questions You Should Practice.


MACHINE LEARNING CAREER

Machine Learning has changed the face of technology as we know it. Machine
Learning adoption results in 3x faster execution and 5x faster decision-making.
As a result, not only are ML engineer positions in high demand, with companies
willing to pay top dollar for the right engineers, but the responsibilities for
these roles have become significantly more diverse.
When a company hires ML engineers, it wants candidates who can contribute to
innovations that will change the world.
1

Machine Learning Job Roles and Responsibilities
Machine Learning Engineers are highly skilled programmers who develop Machine
Learning systems for business applications. They scale prototype models to large
datasets, deploy them on the cloud or internally, and build end-to-end pipelines
to continuously monitor the model performance.
The responsibilities of an ML Engineer differ from one company to the next and
are frequently determined by the size of the company. In this blog, Machine
Learning Engineering Roles — What's the Best Fit for You, you can read about the
differences between different ML roles and determine which is the best fit for
you.
Even though the specific responsibilities of ML Engineers may vary considerably,
their key day-to-day jobs may include all or a subset of the following:
Design and Develop
 * Identifying the specifications for a scalable Machine Learning model for a
   specific business requirement
 * Extracting critical insights from historical data by leveraging
   data-wrangling expertise
 * Analyzing the use cases of ML algorithms and ranking them by their success
   probability
 * Finding the best models to balance business requirements and architectural
   constraints
 * Designing the high-level architecture required to deploy a production scale
   model on a given platform
 * Developing Machine Learning models and tools on petabyte or larger scale
   datasets


Test
 * Identifying differences in data distribution that could affect model
   performance in real-world situations
 * Automating model training and evaluation processes
 * Addressing various bottlenecks in scaling ML models to real-time customers
   with minimum latency and high throughput
 * Collaborating with data scientists and engineers to scale prototype solutions
   and build extensible tools
 * Monitoring model performance on different datasets under different
   architectural constraints
 * Developing pipelines to process and store big data using
   Hadoop/Scala/Spark-like technologies


Deploy
 * Designing and implementing APIs, services that host these models, and
   integrating said services to various endpoints
 * Leveraging AWS (e.g., Sage Maker, Lambda, etc.), Azure, or Google Cloud
   Platform with other techniques (e.g., Spark, Python, Java, etc.) to deploy
   production class ML services
 * Building resilient and transparent end-to-end pipelines to monitor the
   quality and performance of Machine Learning models


Maintain
 * Maintaining a highly scalable data and model management infrastructure that
   supports cutting-edge research
 * Maintaining core system features, services, and engines
 * Reviewing existing code for accuracy and consistency with best practices and
   style guidelines
 * Contributing to documentation and educational content for knowledge transfer
 * Triaging and resolving production issues by analyzing the source and impact
   on architecture, operations, and delivery


Improve
 * Training and retraining ML systems and models as needed
 * Building a suitable product feature roadmap by collecting current and future
   requirements
 * Adapting existing algorithms to make use of parallelized or distributed
   processing systems (e.g., distributed clusters, multicore SMP, and GPU)
 * Building prototypes and A/B Test pipelines to evaluate algorithm improvements


You will work on more and more of the above tasks as you progress in your career
as an ML Engineer. However, if you transition into a managerial role, you can
also expect to:
 * Interact directly with customers to understand their requirements and drive
   changes to product features
 * Advise and collaborate with cross-functional teams, including researchers,
   data scientists, and data engineers, to improve architecture, design, and
   technical capabilities
 * Identify new products and opportunities for the company and influence the
   relevant stakeholders to prioritize their development
 * Develop and manage metrics, KPIs, and dashboards to improve team efficiency
   and ensure conformation to best practices
 * Understand industry-wide trends, and collaborate with industry experts to
   further organizational goals
 * Effectively communicate complex features & systems in detail
 * Mentor and support team members and accelerate their career growth

2

Machine Learning Job Requirements and Skills
A robust coding background with experience in infrastructure design and
end-to-end ML model deployment

In-depth knowledge of various ML techniques, their tradeoffs, their advantages
in terms of performance, and intuitive understanding of which technique fulfills
the need of the hour

Awareness of the latest developments in ML/MLOPs and the ability to iteratively
improve model performance

Confused between Data Science and Machine Learning? Read Machine Learning vs.
Data Science — Which Has a Better Future?
3

Qualifications Required to Become a Machine Learning Engineer
Basic Qualifications
 * Bachelor’s degree or Master’s degree in Computer Science or related field
 * Experience building large-scale machine-learning infrastructure
 * Experience with at least one modern language such as Java, C++, or C#,
   including object-oriented design
 * Hands-on experience deploying Machine Learning models in production
 * Experience with Machine Learning techniques such as pre-processing data,
   training, and evaluation of classification and regression models, and
   statistical evaluation of experimental data.
 * 1+ years of experience contributing to new and current systems' architecture
   and design (architecture, design patterns, reliability, and scaling)


Preferred Qualifications
 * Master's degree in Computer Science or related field
 * Advanced knowledge of performance, scalability, enterprise system
   architecture, and engineering best practices
 * Academic and/or industry experience with one or more domains: computer
   vision, deep learning, Machine Learning, or large-scale distributed systems


Wondering how to list skills on your resume? Read Machine Learning Engineer
Resume Guide: Tips, Best Formats, and Sample Included.
4

Machine Learning Career Roadmap
In a Tier-1 company, the typical career ladder for the ML role looks like this:



MACHINE LEARNING ENGINEER SALARY AND LEVELS AT FAANG+ COMPANIES

Before moving on to FAANG+ companies, here are the average salaries of ML
engineers based on tenure and level in tech companies:
 * ML Engineer I / Entry Level (L3)
 * Years of experience: 0-2
 * Compensation: $190K+
 * ML Engineer II / ML Scientists (L4)
 * Years of experience: 2-5
 * Compensation: $260K+
 * Senior ML Engineers / Applied Scientists / Research Scientists (L5)
 * Years of experience: 5-8
 * Compensation: $360K+
 * Staff ML Engineers / Team Leads (L6)
 * Years of experience: 8-15
 * Compensation: $500K+
 * Principal ML Engineers / ML Directors (L7)
 * Years of experience: 15+
 * Compensation: $850K+

Facebook Machine Learning Engineer Salary
Machine Learning Engineer roles at Facebook are highly rewarding, both in terms
of compensation as well as professional growth. The different levels of Machine
Learning Engineers at Facebook are:
E3 (Associate ML Engineer): This is typically the level at which fresher Machine
Learning Engineers or Software Engineers are hired.

E4: Those hired at this level should have 3-5 years of industry experience.
However, new grads can also be hired at this level, provided they can
demonstrate skill and expertise. 

E5: ML Engineers hired at E5 have at least 5-8 years of industry experience as
they are required to lead complex projects on their own. Also considered the
“terminal” level before an ML Engineer moves into the management domain as E5
onwards, they perform more managerial responsibilities.

E6: Most ML Engineers working at this level have almost 8-15 years of
experience.

E7: This tier is mostly for ML Directors and Principal ML Engineers with more
than 15 years of experience.

Based on these levels, the median Facebook Machine Learning Engineer salary
range is as follows:
Machine Learning Engineer at Facebook
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
E3
US$185K
US$123K
US$40K
US$15K
E4
US$275K
US$166K
US$85K
US$20K
E5
US$411K
US$200K
US$175K
US$30K
E6
US$605M
US$233K
US$310K
US$48K
E7
US$990K
US$278K
US$627K
US$70K
Amazon Machine Learning Engineer Salary 
Being one of the biggest tech companies in the world, Amazon offers lucrative
compensation packages to ML engineers. Amazon has its own Machine Learning
Engineer job levels. They are:
MLE I: Entry-level ML Engineers with less than 4 years of experience pursuing
advanced degrees. They need to be skilled in at least one scripting language and
familiar with SQL.

MLE II: Mid-level ML Engineers have 4-7 years of experience and may also have
the title of ML Engineer II. At this level, ML Engineers usually have a Master’s
degree with a good knowledge of coding.

MLE III: This level is for ML Engineers who have advanced degrees like Ph. Ds in
Machine Learning, Natural Language Processing, etc., based on their area of
specialization. The level includes several managerial positions as well. 

Principal MLE: This level is for ML Engineers with 10+ years of experience.
These employees have several management responsibilities and essentially run the
teams.

Senior Principal MLE: These are highly experienced people who essentially are
team heads with multiple teams working with them in a single or even multiple
product categories.

Based on these levels, the median Amazon Machine Engineer Salary range is as
follows:
Machine Learning Engineer at Amazon
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
MLE I
US$180K
US$135K
US$24K
US$20K
MLE II
US$283K
US$160K
US$85K
US$60K
MLE III
US$370K
US$160K
US$170K
US$128K
Principal MLE
US$700K
US$160K
US$356K
US$214K
Senior Principal MLE
US$900K
US$270K
US$630K
NA
Apple Machine Learning Engineer Salary
The race to get a Machine Learning job at Apple is quite competitive as the
company is renowned for building world-class innovative products. The typical
entry-level Apple Machine Learning Engineer’s salary is $180k per year.
The company divides the ML Engineer roles into different levels:
ICT2: Apple’s entry-level position which usually attracts people with 0-1 year
of experience. They need to have at least some knowledge of ML modeling with
proficiency in Python.

ICT3: People hired at this level should have around 2-5 years of experience with
demonstrated knowledge of ML model deployment. Master’s degree holders can
usually start out at this level.

ICT4: This level is for people with 5-10 years of experience or a Ph.D. in a
related field like Computer Science, Machine Learning, etc. Managerial positions
also start at this level.

ICT5: Senior ML Engineers with 10+ years of experience are hired at this level.
They are expected to manage their own teams within the organization or work with
cross-functional teams.

ICT6: Highly experienced people with experience in managing multiple teams are
usually hired at this level.

Based on these levels, the median Apple Machine Learning Engineer Salary range
is given below:
Machine Learning Engineer at Apple
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
ICT2
US$180K
US$130K
US$30K
US$20K
ICT3
US$240K
US$155K
US$65K
US$20K
ICT4
US$345K
US$195K
US$125K
US$23K
ICT5
US$472K
US$227K
US$200K
US$50K
ICT6
US$990K
US$280K
US$650K
US$60K
Netflix Machine Learning Engineer Salary
Unlike other companies such as Amazon and Apple, Netflix doesn’t have job
levels. The company is known mostly for hiring only senior professionals with at
least 4 years of experience. They have also started hiring new graduates for
software engineer positions recently. 
Here are the median salaries of a Software Engineer at Netflix working in the
ML/AI domain:

Machine Learning Engineer at Netflix
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
New Grad Software Engineer
US$240K
US$180K
US$60K
$13K
Senior Software Engineer
US$675K
US$645K
US$30K
$13K
Google Machine Learning Engineer Salary
At the helm of today’s Machine Learning innovation is Google. So when the
company sets out to hire Machine Learning engineers, you know they are looking
for only the best of the best. The typical entry-level Google Machine Learning
Engineer’s salary is $196K per year.
The different job levels at Google:
L3 (ML Engineer II): An entry-level position with 0-1 year of experience

L4 (ML Engineer III): Requires 2-5 years of experience

L5 (Senior ML Engineer): Requires over five years of experience

L6 (Staff ML Engineer): Requires 5-8 years of experience

L7 (Senior Staff ML Engineer): Requires over 8 years of experience

Machine Learning Engineer at Google
Average compensation by level
Level name
Total
Base
Stock (/yr)
Bonus
L3
US$196K
US$138K
US$40K
US$21K
L4
US$283K
US$169K
US$85K
US$29K
L5
US$364K
US$190K
US$134K
US$35K
L6
US$535K
US$232K
US$240K
US$53K
L7
US$730K
US$272K
US$375K
US$80K
Machine Learning Engineer Salaries at Other Tech Companies
Knowing the Machine Learning Engineer's salary details for other tier-1
companies can help you evaluate your options better. We’ve curated the salaries
associated with each of these companies at different levels:
Machine Learning Engineer at Tier-1 Companies
Average compensation by level
Company
Level Name
Total Compensation
Years of Experience
Adobe
Software Engineer 1
Software Engineer 2
Software Engineer 3
Software Engineer 4
Software Engineer 5
Software Engineer 5.5
US$200K
US$220K
US$245K
US$324K
US$430K
US$667K
0-1
1-2
2-5
5-8
8-10
10+
Airbnb
L3
L4
L5
US$266K
US$295K
US$447K
0-1
1-4
4-10
DoorDash
E3
E4
E5
US$200K
US$330K
US$380K
0-2
2-5
5+
IBM
Associate Engineer
Staff Engineer
Advisory Engineer
Senior Engineer
Senior Technical Staff Member
Distinguished Engineer
US$100K
US$136K
US$160K
US$232K
US$270K
US$367K
0-1
1-3
3-8
8-12
12-16
16+
IBM
Associate Engineer
Staff Engineer
Advisory Engineer
Senior Engineer
sr.Technical Staff Member
Distinguished Engineer
US$100K
US$136K
US$160K
US$232K
US$270K
US$367K
0-1
1-3
3-8
8-12
12-16
16+
LinkedIn
Software Engineer
Senior Software Engineer
Staff Software Engineer
Senior Staff Software Engineer
US$250K
US$312K
US$522K
US$671K
0-3
3-8
8-13
13+
Microsoft
59, 60
61, 62
63, 64, 65
66, 67
68
US$170K
US$200K
US$320K
US$445K
US$700K
0-3
3-5
5-8
8-12
12+
Pinterest
L3
L4
L5
US$230K
US$285K
US$465K
0-2
2-3
3-8
Twitter
SWE I
SWE II
Senior SWE
Staff SWE
Senior Staff SWE
US$193K
US$255K
US$333K
US$590K
US$600K
0-1
1-3
3-6
6-10
10+
Uber
Software Engineer I
Software Engineer II
Senior Software Engineer
Staff Software Engineer
Senior Staff Software Engineer
US$164K
US$260K
US$450K
US$530K
US$800K
0-1
1-3
3-8
8-12
12+
Zillow
P2
P3
P4
P5
US$170K
US$240K
US$350K
US$505K
0-1
1-3
3-6
6+
You can learn more about more related topics on our companies page.


FAQS ON MACHINE LEARNING INTERVIEW COURSE

1
What are the programming languages used in Machine Learning?

Machine Learning modeling is typically done in Python, which has excellent
support from inbuilt libraries to do the same. R is another programming language
used for experimentation purposes, but it's not as widely used as Python. Some
companies might also use MATLAB.
2
Is having a mathematics background a must for ML-related roles?

While it is not a must, having familiarity with concepts such as probability,
integrals, differentiation, vectors, coordinate geometry, etc., can assist in
understanding the idea behind several ML algorithms.
3
Do ML Engineers perform ML modeling/experimentations, or are they just concerned
with the deployment part?

It depends on the role. Many companies expect MLEs to handle modeling,
experimenting, and deployment parts. In contrast, other companies have data
scientists to perform ML experiments and MLEs to translate those python ML
models to binaries for deployment.
4
Is IK’s Machine Learning Interview Course just for professionals working as ML
Engineers in non-FAANG+ companies?

No, this course is for everyone – FAANG or non-FAANG. If you have worked as an
ML Engineer in any company, or you have relevant background in ML production and
design, this course is for you. We will help you in all the preparation that you
need for cracking the ML Engineer roles in any company.
5
I am working as a Data Scientist in my current company. Will this course help me
transition into an ML Engineer role?

That depends. If you have some practical experience in deploying Machine
Learning models on a production scale with working knowledge of platforms like
AWS, Azure, or GCP, then this course can help you fill in the gaps required for
an ML Engineer role. We will cover the relevant Data Structures and Coding,
Scalable System Design, and ML System design concepts that you will need to
crack the interviews. Additionally, we will also help you modify your resume to
highlight your ML Engineer relevant experience to recruiters.
6
Is this Machine Learning Interview course suitable for freshers?

No, this course is for working professionals with at least two years of
experience working as an ML Engineer or Software Engineer working on ML
projects. Additionally, if you are a Data Scientist with practical experience in
deploying ML models, you can join the course to transition into the ML Engineer
role.
7
Why do we need to learn Scalable System Design concepts for an ML Engineer
interview?

Scalable system design, specifically ML system design, is an integral part of
this role. ML Engineers are required to go through at least 1 or 2 system design
rounds. ML Engineers build on the concepts learned from deploying a general
software and combine it with the knowledge of ML algorithms to deploy ML models
on a production scale. In our course, we cover both scalable system design in 3
weeks and ML system design in every live class of the ML Masterclass.
8
How hard are the coding questions asked in ML Engineer interviews?

The coding question difficulty depends on what level and role you are applying
for. Typically, entry-level MLE roles would require you to answer medium
difficulty questions with some hard problems thrown in.
Medium to senior level roles would test you on medium-hard to hard problems.
However, if you are applying for an Applied Scientist or Research Scientist
position, the coding bar is a lot lower, and you will be asked easy to medium
difficulty questions. The coding round can also be skipped if you have 20+ years
of experience and for certain Managerial positions.


HOW TO ENROLL FOR THE MACHINE LEARNING INTERVIEW COURSE?

Learn more about Interview Kickstart and the Machine Learning course by joining
the free webinar hosted by Ryan Valles, co-founder of Interview Kickstart. You
can also talk to our program advisors to get additional program-related details.
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