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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
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:
1-3 ML technical rounds (ML breadth and depth understanding):
Deployment Tools and techniques
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.
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.
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.
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
ML Engineer II / ML Scientists (L4)
Years of experience: 2-5
Senior ML Engineers / Applied Scientists / Research Scientists (L5)
Years of experience: 5-8
Staff ML Engineers / Team Leads (L6)
Years of experience: 8-15
Principal ML Engineers / ML Directors (L7)
Years of experience: 15+
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.
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.
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.
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
New Grad Software Engineer
Senior Software Engineer
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
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:
What are the programming languages used in Machine Learning?
Is having a mathematics background a must for ML-related roles?
Do ML Engineers perform ML modeling/experimentations, or are they just concerned with the deployment part?
Is IK’s Machine Learning Interview Course just for professionals working as ML Engineers in non-FAANG+ companies?
I am working as a Data Scientist in my current company. Will this course help me transition into an ML Engineer role?
Is this Machine Learning Interview course suitable for freshers?
Why do we need to learn Scalable System Design concepts for an ML Engineer interview?
How hard are the coding questions asked in ML Engineer interviews?
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.