Netflix Machine Learning Engineer (MLE) Interview Guide

The gist

Netflix is renowned for its results-focused, performance-oriented company culture. They consider their company and employees “a professional sports team, not a family.” Netflix refers to its staff internally as the Dream Team and sets particularly demanding standards to match. For showing exemplary performance and teamwork, employees earn promotions and the best salary in the industry; for what Netflix considers adequate performance, you can expect a brief tenure and a generous severance package.

While Netflix insists on the best and prunes accordingly, this doesn’t mean they cultivate a dog-eat-dog office culture full of “brilliant jerks”—just the opposite, in fact. Netflix views teamwork as paramount to success, so candidates who bring a toxic attitude to the workplace will not be tolerated.

Netflix operates on a massive scale, streaming data-rich media without interruption to hundreds of millions of users worldwide. Their ML engineers must be able to build and work with systems that handle this data reliably and cheaply.

What does a Netflix ML Engineer do?

Netflix expects their engineers to be proficient in Python and at least one of Scala, Java, C++ or C#. When it comes to ML, Netflix looks for candidates who have a successful track record of training, deploying, and tuning models in a commercial setting. In keeping with their results-first philosophy, Netflix focuses on A/B tests, experimentation, and establishing clear measures of business impact.

Machine learning algorithms are applied to all sorts of tasks at Netflix. They power the personalized recommendations offered to individual users and are used to extract information about the kind of content viewers are interested in. These insights, in turn, influence what Netflix studios and content distributors focus on.

Netflix uses ML tools to analyze how its users behave as a group, not just individually. This means that machine learning engineers at Netflix even play a part in determining optimal shooting schedules and locations for new shows. For example, Netflix uses ML to determine what times and regions will put the most strain on its servers, or how its userbase is likely to change over time.

Though historically one of the legendary “Big Tech” companies, Netflix’s team is only about one-sixth as large as its peers. To be part of the Dream Team, you’ll be expected to take on more responsibilities than at most tech firms. Engineers who succeed at Netflix are ambitious and proactive, and they work well with teammates from a variety of departments. (Besides other SWEs, you’ll interact regularly with researchers, data scientists, and product managers.)

One of the company mottos is “People over Process.” Micromanagement and bureaucracy at Netflix are kept to a minimum; instead, leaders are expected to hear out a range of viewpoints, set an informed goal for their team, and provide the context and information that ICs need to tackle problems creatively.

Because Netflix wants to attract and retain premium talent, their compensation is among the highest in the industry (compared to those offered for similar roles at other companies). Each Netflix employee gets to choose how their comp will be proportioned each year between salary and NFLX stock options; the company does not award bonuses.

Netflix only introduced official “levels” for its software engineers in 2022, after 25 years of an unusually flat structure in which all Netflix engineers were labeled internally as “Senior.” The average compensation at each level is as follows:

• L3 (Software Engineer I): ~$220k
• L4 (Software Engineer II): ~$332k
• L5 (Senior Software Engineer): ~$525k
• L6 (Staff Software Engineer): ~$800k
• L7 (Principal Software Engineer): ~$1.17M

Before you apply

1. Become familiar with Netflix’s core values and be ready to demonstrate them. It’s also a good idea to look over the Netflix culture deck. Culture fit is make-or-break at Netflix, and it will be a major focus of multiple interview rounds as your application moves forward.
2. Research the Netflix service assigned to the team you’re applying to work on. Technical screens will focus primarily on skills and expertise relevant to the tasks you’ll face on the job. Check out Netflix’s technology blog and published research to get a sense of the tools and technical challenges their engineers are tackling, and think of ways to connect your previous experience to problems you may be asked about in the interview.
3. Refresh your knowledge of machine learning algorithms and systems, including how they perform in real-world situations. The upcoming tech screens will test both your conceptual knowledge and your ability to weigh trade-offs when developing a new product.
4. Book a mock interview with an ML engineer at the level you aspire to so you can express your expertise confidently under pressure.

Interview process

The hiring process for new engineers at Netflix usually consists of 3 stages of interviews:

• At least one initial phone screen with a recruiter or hiring manager
• A technical screen in the form of an online assessment (or alternatively, another hour-long call with a Netflix engineer)
• An interview loop of 4–8 rounds, usually held on-site for the role

As one might expect from Netflix’s “(almost) no rules rule,” each team at the company is free to set its own standards for who it will hire. Because of this, the exact sequence of interviews may vary, and the challenges at each step will depend on who’s in charge of hiring for the role you’re applying to.

Phone screen(s)

The first step after you apply is usually a phone call with a recruiter, which usually lasts about 30 minutes. They’ll ask about your background and discuss the role and details of the upcoming interview process. This is also when they’ll evaluate your potential culture fit. Netflix takes its core values very seriously—familiarize yourself with them beforehand and be ready to illustrate how you’ll fulfill them.

Sample questions include:

Why do you want to work at Netflix?

Asked at Netflix 

See question

• What stands out to you about our culture?
• Tell me about yourself.
• Tell me about the most challenging situation in your career and how you handled it.

You might also be scheduled for a call with the hiring manager for the role you’re applying to. (This may come several days after the recruiter call.) In this conversation, the hiring manager will ask more specific questions about your skills and experience, particularly when it comes to projects using ML. They’ll use this call to describe the role and their team in greater detail, making it an excellent chance for you to ask questions about what working with them will be like.

Sample questions include:

• Tell me about a machine learning project you worked on.
• In your previous work with ML models, how did you test them to ensure that they were working as intended?

Technical screen

The next step of the process varies a lot between specific teams at Netflix, as each one chooses which topics and tools will be covered. The technical screen will likely be done in a 45–60 minute telephone interview with a Netflix engineer, although you may be given the choice to complete an online technical assessment instead. If you go with the online assessment, expect several multiple-choice questions and a couple of ML coding problems.

Whether your technical screen is done on the phone or online, you can expect both options to be similarly comprehensive. Be prepared for many questions about ML algorithms and concepts, and how to apply them in a real-world setting.

Sample questions include:

• How do you decompose ML model errors into variance and bias?
• Given an integer N, how would you write a function that returns a list of all the prime numbers up to N?

Final interviews

The final step to complete is a gauntlet of up to six 30-minute interview rounds on-site. As with the technical screen, implementation can vary. There are some constants to the process, however.

At least one of these rounds, usually one of the first, will involve more technical screening and problem-solving. For an ML role, this will include questions about machine learning, A/B tests and experiments, and practical system design. A coding challenge is also possible. These problems tend to reflect situations and obstacles that Netflix engineers have to deal with on the job, as opposed to more abstract CS concepts—but it’s important to know those, too, especially ones related to ML algorithms and information retrieval.

Sample questions include:

• How would you build a system like Netflix?
• You’ve been given access to 10,000 movie reviews. Each review contains several sentences and a score from 1 to 10. How would you design a system to predict the movie score based on the review text?
• Given a dictionary with weights, write a function in Python that returns a key at random, with a probability proportional to the weights.

The remaining rounds are all about assessing your culture fit. You’ll meet with a member of Netflix HR, the hiring manager for your team, and almost always a “partner” manager from another team. Each one will ask you questions about your ability to fulfill the values that Netflix expects from their Dream Team. One common task that candidates are asked to perform here is to present a 10–15 minute talk on a technical topic of your choice.

Sample questions include:

• How do you make sure that an ML model you build is explainable and transparent to non-technical stakeholders?
• Tell me about a time you failed at an ML project and how you handled it.
• Describe an engineering project where you had to work cross-functionally with other teams.

Interview questions to expect at Netflix

Behavioral

Culture fit is a top priority in Netflix hiring. They’re looking for candidates who display the values of selflessness, sound judgment, candor, creativity, courage, inclusion, curiosity, and resilience.

Throughout the interview process, you’ll be grilled on your ability and experience with handling adversity, ambiguity, and conflict. The Netflix ethos promotes (literally!) employees who are considerate, while still being truthful and direct. One upside of this for you as a candidate is that Netflix culture fit assessments aren’t as subjective or vibes-based as they would be at more informal companies. They have a definitive rubric that they compare against, and they tend to ask clear and to-the-point questions so they can quickly figure out how you measure up to it.

Sample questions include:

• Why do you want to work at Netflix?
• What do you like most about the culture memo, and what would you have done differently?
• What other entertainment or media companies have you worked for?
• Tell me about a time you were honest at work.
• Tell me about a time you gave difficult feedback to a coworker. What happened?
• What do coworkers say about you? Share some positive and negative feedback you’ve received.

Coding and ML knowledge

Raw coding skills are comparatively less emphasized at Netflix compared to behavioral compatibility and system design expertise, but you do need to demonstrate that you’re comfortable coding ML software in Python and manipulating data with SQL. You’llll have to answer coding questions during the technical screen, and may be assigned a couple of CodeSignal programming challenges if you’ve chosen to take an online assessment instead of a phone screen.

Sample questions include:

Explain TF-IDF.

Asked at Netflix 

See question

• How would you implement a collaborative filtering algorithm?
• Explain the pros and cons of using a decision tree vs. a neural network.

ML system design

System design skills are weighted heavily at Netflix. Their services are their business, and they’re competing for a pool of picky customers with many alternatives at their fingertips and a low tolerance for interruptions. Netflix engineers can’t afford to compromise on scalability, reliability, or security.

Your interviewers will present real-world business needs and ask how you’d build or adjust an ML service to address them. Often, these problems will be drawn from challenges the team you’re applying to is dealing with already.

Sample questions include:

• How would you build a system like Netflix?
• What metrics do you monitor after deploying a machine learning model?
• You’ve been given access to 10,000 movie reviews. Each review contains several sentences and a score from 1 to 10. How would you design a system to predict the movie score based on the review text?

Additional resources

• Take our ML Engineering Interview courses to get a leg up on the topics and tools you’ll need to know.
• Check out Netflix’s most recent culture deck (as of June 2024).
• Take a look at Netflix’s famous original culture deck, as presented by co-founder Reed Hastings in 2009.
• Take a peek at Netflix’s published research papers and the Netflix technology blog.
• Netflix’s self-published business outlook will help you answer questions about how your goals, behavior, and values fit into those of Netflix.

FAQs about Netflix ML Engineer interviews

How should I prepare for a Netflix ML Engineer interview?

To prepare for an ML engineer interview at Netflix, internalize the Netflix core values and read their famous culture deck. Brush up on your knowledge of machine learning concepts and tools, and practice explaining them. Prepare examples of how you’ve tackled real-world problems with ML. Take some time to practice coding to spec under pressure.

How much does a Netflix ML Engineer make?

On average, Netflix ML Engineers can expect these ranges for total compensation:

• L3 (Software Engineer I): ~$220K
• L4 (Software Engineer II): ~$332K
• L5 (Senior Software Engineer): ~$525K
• L6 (Staff Software Engineer): ~$800K
• L7 (Principal Software Engineer): ~$1.17M

How long is the Netflix ML Engineer interview process?

It depends on the team, but most candidates complete the Netflix ML Engineer interview process in 3–5 weeks.