Machine Learning COURSE

Nail Your Next Machine Learning Interview

4.65

Students enrolled: 240

Designed and taught by FAANG+ engineers, this course will give you a foolproof preparation strategy to crack the toughest interviews at FAANG and Tier-1 companies.

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Course Overview

Start Learning

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

Students who chose to uplevel with IK got placed at

Safir Merchant

SWE, Machine Learning

Jameson Merkow

Principal AI Engineer

Sayan Banerjee

Data Scientist II

Mike Kane

Lead Data Engineer, Analytics

Akshay Lodha

Data Engineering & Analytics

Anju Mercian

Data Engineering Consultant

Alokkumar Roy

Data Engineer

Siva Karthik Gade

SDE, Machine Learning

Sai Marapa Reddy

SWE, Machine Learning

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Best suited for

Why choose this course?

Program designed by FAANG+ leads

Covering data structures, algorithms, interview-relevant topics, and career coaching

Individualized teaching and 1:1 help

Technical coaching, homework assistance, solutions discussion, and individual session

Mock interviews with Silicon Valley engineers

Live interview practice in real-life simulated environments with FAANG and top-tier interviewers

Personalized feedback

Constructive, structured, and actionable insights for improved interview performance

Career skills development

Resume building, LinkedIn profile optimization, personal branding, and live behavioral workshops

50% Money-Back Guarantee*

If you do well in our course 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.*

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Meet your instructors

Our highly experienced instructors are active hiring managers and employees at FAANG+ companies and know exactly what it takes to ace tech and managerial interviews.

Matt Nickens

Manager, Data Science

10+ years experience

Jameson Merkow

Principal AI Engineer

11+ years experience

Christian Monson

Machine Learning Scientist

9+ years experience

Alireza Dirafzoon

Research Engineer

7+ years experience

A typical week at Interview Kickstart

This is how we make your interview prep structured and organized. Our learners spend 10-12 hours each week on this course.

Thu

Attend online live sessions

Get high-quality videos and course material that introduce the fundamentals and intuition behind the topics covered in the upcoming week's live class

Discusses interview-relevant questions and insights

Assignment review session

Discuss the coding or system design solutions and practice answering frameworks in class

Interact with Tier-1 instructors to get insights into your solution, along with a model solution for each problem covered

Sun

Attend online live sessions

Attend 4-hour interactive sessions covering a new ML System Design problem every week

Get interview-relevant insights into the System Design problem from a Tier-1 tech instructor

Learn how you can approach the problem using a comprehensive answering Framework

Mon-Wed

Coding assignments

Solve specially curated coding assignments where you implement the learnings from the week's module

System design assignments

Apply the framework taught in the live class to solve a new Scalable/ML System Design problem

Discuss the solution in a mock group session with a Tier-1 instructor

Technical coaching sessions

Clear technical or interview-specific doubts (if any) with FAANG+ instructors.

Every day

1:1 access to instructors

Personalized coaching from FAANG+ instructors

Individualized and detailed attention to your questions

Solution walkthroughs

Machine Learning Interview Course Curriculum

Data structures and Algorithms

Sorting

Introduction to Sorting
Basics of Asymptotic Analysis and Worst Case & Average Case Analysis
Different Sorting Algorithms and their comparison
Algorithm paradigms like Divide & Conquer, Decrease & Conquer, Transform & Conquer
Presorting
Extensions of Merge Sort, Quick Sort, Heap Sort
Common sorting-related coding interview problems

Recursion

Recursion as a Lazy Manager’s Strategy
Recursive Mathematical Functions
Combinatorial Enumeration
Backtracking
Exhaustive Enumeration & General Template
Common recursion- and backtracking-related coding interview problems

Trees

Dictionaries & Sets, Hash Tables
Modeling data as Binary Trees and Binary Search Tree and performing different operations over them
Tree Traversals and Constructions
BFS Coding Patterns
DFS Coding Patterns
Tree Construction from its traversals
Common trees-related coding interview problems

Graphs

Overview of Graphs
Problem definition of the 7 Bridges of Konigsberg and its connection with Graph theory
What is a graph, and when do you model a problem as a Graph?
How to store a Graph in memory (Adjacency Lists, Adjacency Matrices, Adjacency Maps)
Graphs traversal: BFS and DFS, BFS Tree, DFS stack-based implementation
A general template to solve any problems modeled as Graphs
Graphs in Interviews
Common graphs-related coding interview problems

Dynamic Programming

Dynamic Programming Introduction
Modeling problems as recursive mathematical functions
Detecting overlapping subproblems
Top-down Memorization
Bottom-up Tabulation
Optimizing Bottom-up Tabulation
Common DP-related coding interview problems

System Design

Online Processing Systems

The client-server model of Online processing
Top-down steps for system design interview
Depth and breadth analysis
Cryptographic hash function
Network Protocols, Web Server, Hash Index
Scaling
Performance Metrics of a Scalable System
SLOs and SLAs
Proxy: Reverse and Forward
Load balancing
CAP Theorem
Content Distribution Networks
Cache
Sharding
Consistent Hashing
Storage
Case Studies: URL Shortener, Instagram, Uber, Twitter, Messaging/Chat Services

Batch Processing Systems

Inverted Index
External Sort Merge
K-way External Sort-Merge
Distributed File System
Map-reduce Framework
Distributed Sorting
Case Studies: Search Engine, Graph Processor, Typeahead Suggestions, Recommendation Systems

Stream Processing Systems

Case Studies: on APM, Social Connections, Netflix, Google Maps, Trending Topics, YouTube

Machine Learning Masterclass

Supervised Learning I - Rank Relevant Search Results

Deep dive into the design of a search relevance system like Google Search (a popular FAANG interview question).
Comprehensive coverage of document indexing, retrieval, similarity scoring, filtering, and ranking.
Model training: Build a ranking model using Linear or Logistic Regression. Which method performs better for ranking a search result?
Online testing scenarios: How to run an A/B test to evaluate if new features improve the model? Which metrics to use?
Interesting follow-up questions: How do you offset the lack of negative samples for training without adding new data? Does a “no-click” impression correspond to a negative label?
Non-trivial questions on fundamental topics: Does L1/L2 regularization always increase model performance? Which metric is best for evaluating models on imbalanced datasets? When is the k-nearest neighbors algorithm better than logistic regression for classification analysis?

Supervised Learning II - Design a YouTube Video Recommendation System

Build a video recommendation system for YouTube users; Answer questions like – How to maximize user engagement? How to recommend new content to users?
Best practices of feature engineering, data collection, feature encoding, and video embeddings.
In-depth coverage of content-based and collaborative filtering, matrix factorization, and maximizing the optimum objective function.
Triage critical concerns when building a recommender system: How do you account for positional bias when ranking a video? Strategies to ensure the freshness, diversity, and fairness of the recommendations.
Non-trivial questions on fundamental topics: How do you train a State Vector Machine (SVM) on non-linear data? Why only a subset of features for each tree in a random forest? What is the bias-variance tradeoff, and how can ensemble learning techniques like bagging and boosting address it?

Unsupervised Learning - Detect Fraud Transactions for Airbnb

Design an anomaly detection system for Airbnb transactions.
Comprehensive coverage of fraud detection techniques: Reputation lists, rules-based detection, classification vs. clustering.
A top-down approach to building a high-level architecture: User and agent data, feature aggregation, model dashboard, data embedding
Difficult follow-up questions: How To speed up computation time for unsupervised anomaly detection? Strategies to combine clustering with supervised learning techniques.
Non-trivial questions on fundamental topics: What could be the possible reason(s) for producing two different dendrograms using an agglomerative clustering algorithm for the same dataset? Dimensionality reduction with computational power constraints – t-SNE vs. PCA??

Deep Learning I - Detect and Process Objects in a Scene

Design an Image Processing system for Object Detection – frequently asked in FAANG ML interviews.
Deep-dive into object detection workflow: Preprocessing, Candidate Generation and Selection, Unprocessing, and Postprocessing.
Multiple strategies to build the object detector: Convolutional Networks, Region-based CNNs, You Only Look Once (YOLO), Transfer Learning, etc.
Interview-relevant strategies: What questions to ask an interviewer? How to structure your solution?
Address possible follow-up questions: How do you detect and replace multiple instances of the same object from an image? What if direct ground truth labels are absent?
Non-trivial questions on fundamental topics: Using dropout layers in a small neural network. How to combat vanishing and exploding gradients in CNNs? What is the best learning rate optimizer for improving model performance on large datasets?

Deep Learning II - Build a Tech Support Chatbot

Design an intelligent Discord bot to provide Technical Support for a software Bootcamp.
In-depth coverage of functional and non-functional requirements: Scale and latency estimation, throughput, passive feedback mechanisms
Knowledge base creation: Embedding, Sharding, Caching, etc. Strategies to expand the knowledge base.
Challenges in bot design: How to deal with a cold-start with no knowledge base? How do you generate answers to previously unasked questions?
Logical follow-up questions: How to handle increasing complexity and scale? How can we introduce a continuous learning mechanism in the chatbot design?
Non-trivial questions on fundamental topics: Which type of word embedding method is more suitable for measuring context similarity? Why are “Exploding Gradients” a problem in the context of RNNs? When should we not use a bi-directional LSTM?

Additional Topics:

A comprehensive step-by-step approach to ML System Design interview rounds
- How is ML System Design different from General System Design?
- What does an interviewer expect from this round?
Modern ML Architectures
- Why do we learn a distribution instead of a deterministic model during encoding? How do we introduce variability in a variable autoencoder?
- What is the difference between Discriminative and Generative models?
Reinforcement Learning
- How do you evaluate the state and responses of an agent?
- How is value iteration different from policy iteration? What problem does it address?

Career Coaching

Interview strategy and success

Behavioral interview prep

Offers and negotiation

Support Period

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching

Interview strategy and salary negotiation support

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Practice and track progress on UpLevel

UpLevel will be your all-in-one learning platform to get you FAANG-ready, with 10,000+ interview questions, timed tests, videos, mock interviews suite, and more.

Mock interviews suite

On-demand timed tests

In-browser online judge

10,000 interview questions

100,000 hours of video explanations

Class schedules & activity alerts

Real-time progress update

11 programming languages

Get upto 15 mock interviews with

hiring managers

What makes our mock Interviews the best:

Hiring managers from Tier-1 companies like Google & Apple

Interview with the best. No one will prepare you better!

Domain-specific interviews

Practice for your target domain - Machine Learning

Detailed personalized feedback

Identify and work on your improvement areas

Transparent, non-anonymous interviews

Get the most realistic experience possible

More about mock interviews

1. Flexible schedule

Pick timings convenient to you

4. Technical and behavioral interviews

Uplevel your technical and behavioral interview skills

2. Remote interview experience

Mirrors the current format of remote interviews

5. Level-specific interviews

Because an L4 at Google can be quite different from an E7 at Meta

3. Feedback documentation

All the feedback you’ve ever wanted, recorded and documented

6. Interviewer of your choice

Choose based on domain

Career impact

Our engineers land high-paying and rewarding offers from the biggest tech companies, including Facebook, Google, Microsoft, Apple, Amazon, Tesla, and Netflix.

Sumanth

Works at:

Simply Awesome Comprehensive ML Course from IK I am a Backend Software Engineer who has taken the Uplevel Machine Learning course which unlike other programs is a very thorough and intense one year long course. The value, the students get out of this year long course is simply amazing as it is like a Master's Course which you do at a regular University. Thanks for coming up with such a course which is beneficial to all Engineers who want to up level themselves in AI&ML.

Arul

Works at:

Interview Kickstart (IK) offers valuable tech interview preparation with detailed technical modules, practical mock interviews, and excellent support. Mock Interviews: IK's mock interviews simulate real interview environments at top tech companies, providing immediate, actionable feedback. Technical Modules: The modules, especially in Data Structures & Algorithms and System Design, are well-constructed and taught by knowledgeable instructors with industry experience. Overall, IK refined my preparation for technical interviews, and I am grateful to the IK team.

Anup Bhatt

Works at:

Classes: I attended the live classes for asking doubts and professional interaction, it was really great.
Mock Interviews: This is the main reason for spending on this product. You get FAANG(MAANG now) or equivalent level engineers and managers preparing you for actual interviews through proper simulation of real-world conditions
Support staff: In my experience, the support staff were quite prompt and got all my requests sorted by 24 to 48 hours turnaround time.
Discord/Alumni Groups: This is where the networking and extra stuff happens. Highly recommend this activity !

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.

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

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.

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:

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.

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.

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?

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.

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.

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

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?

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.

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

US$185K

US$123K

US$40K

US$15K

US$275K

US$166K

US$85K

US$20K

US$411K

US$200K

US$175K

US$30K

US$605M

US$233K

US$310K

US$48K

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

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

US$196K

US$138K

US$40K

US$21K

US$283K

US$169K

US$85K

US$29K

US$364K

US$190K

US$134K

US$35K

US$535K

US$232K

US$240K

US$53K

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

US$266K

US$295K

US$447K

0-1

1-4

4-10

DoorDash

US$200K

US$330K

US$380K

0-2

2-5

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+

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

US$170K

US$200K

US$320K

US$445K

US$700K

0-3

3-5

5-8

8-12

12+

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

US$170K

US$240K

US$350K

US$505K

0-1

1-3

3-6

You can learn more about more related topics on our companies page.

FAQs on Machine Learning Interview Course

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.

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.

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.

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.

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.

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.

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.

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.

Nail Your Next Machine Learning Interview

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Course Overview

Start Learning

Students who chose to uplevel with IK got placed at

21,000+

Tech professionals trained

$1.28M

Highest offer received by an IK alum

53%

Average salary hike received by alums

Best suited for

Why choose this course?

Program designed by FAANG+ leads

Individualized teaching and 1:1 help

Mock interviews with Silicon Valley engineers

Personalized feedback

Career skills development

50% Money-Back Guarantee*

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Meet your instructors

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Jameson Merkow

Christian Monson

Alireza Dirafzoon

A typical week at Interview Kickstart

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Every day

Machine Learning Interview Course Curriculum

Sorting

Recursion

Trees

Graphs

Dynamic Programming

Online Processing Systems

Batch Processing Systems

Stream Processing Systems

Supervised Learning I - Rank Relevant Search Results

Supervised Learning II - Design a YouTube Video Recommendation System

Unsupervised Learning - Detect Fraud Transactions for Airbnb

Deep Learning I - Detect and Process Objects in a Scene

Deep Learning II - Build a Tech Support Chatbot

Additional Topics:

Interview strategy and success

Behavioral interview prep

Offers and negotiation

15 mock interviews

Take classes you missed/retake classes/tests

1:1 technical/career coaching

Interview strategy and salary negotiation support

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