How to Nail your next Technical Interview

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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.

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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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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+

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+

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

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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How to Nail your next Technical Interview

Transition to Engineering Manager roles

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Students who chose to uplevel with IK got placed at

Management SwitchUp course curriculum

Transitioning from IC to Manager

Understanding EM Interview Process

Key Leadership Traits

Dealing with ambiguity as a new EM

How to position as an EM compared to your peers

Getting team onboard post new reorganisation/new strategy

Managing remote/hybrid team

1:1 with team members

Tactics to understand and monitor team health

Supporting team members in their career development

Creating pods/working groups within team

Core Engineering Metrics for team functions

Creating quarterly/half yearly goals for your team

Workload Balancing

Stakeholder Management

Conveying challenging feedback and receiving them

Setting Project Retrospective

Feedback Management

Resource Management

Skill Development

Project Experience

Motivations and Values

Leadership Profile

Ice Breakers

Personal Growth

Conflict Resolution

Execution and Planning

Cross-Functional Skills

Basics of Software System Design

ML Design Principles

ML Project Scoping

ML Model Deployment

Advanced Machine Learning

MLOPs

Industry-relevant projects, replicating real-life projects at Tier-1 companies

PART 2: Interview Preparation

Trees

Graphs

Greedy Algorithms

Dynamic Programming

Online Processing Systems

Batch Processing Systems

Stream Processing Systems and Object Modeling

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

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Individualized teaching and 1:1 help

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Personalized feedback

Mock Interviews with FAANG+ Managers

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Danielle Class

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