Snap Machine Learning Engineer (MLE) Interview Guide

According to Snap, the maker of the popular social media app Snapchat, "reinventing the camera represents our greatest opportunity to improve the way people live and communicate."

This core belief drives Snap in developing its social media platform as well as experimental augmented reality (AR) experiences. From its entertaining camera filters to its high-tech Spectacles to the AR platform Lens Studio, Snap’s user-centric products would not be possible without machine learning engineers.

If you’re interested in using your machine learning expertise to create and improve beloved social products, a Snap MLE role might be for you.

Below, we dive into the Snap machine learning engineer interview process and provide insider tips on how to get hired.

What does a Snap MLE do?

At Snap, machine learning engineers play an important role in advancing its products, which include:

• Snapchat, a visual messaging app for connecting with friends, family, and others around the world
• Lens Studio, an augmented reality platform that powers AR across Snapchat and other services
• Spectacles, smart glasses that can record videos and bring AR to life

Snap MLEs apply their expertise in machine learning algorithms and techniques to build recommendation systems, develop generative AI models, and improve Snapchat’s search functionality as well as other features.

Day-to-day work for most Snap roles is focused on creating and fine-tuning the models behind Snap’s products.

Whether it's writing code to serve more relevant Discover content on Snapchat or to improve the platform’s security architecture, Snap's machine learning engineers collaborate closely with other cross-functional teams.

According to the Snap MLE we spoke with, it’s very common to work alongside other MLEs as well as software engineers and non-technical team members.

In terms of compensation, the average Snap MLE salary ranges from $226,000 to $346,000 per year, including bonus and stock.

What are the typical job requirements for a Snap MLE?

Education: Although an advanced degree doesn’t hurt, most MLE roles only require a bachelor's degree in computer science, mathematics, statistics, or another relevant field. According to our source, years of experience are generally more important to Snap than formal education.

Experience: MLE roles generally require at least 5-8 years of experience, a background in machine learning approaches and algorithms, and strong collaboration and mentorship skills.

Previously, Snap used a general MLE job posting in which, after receiving an offer, successful candidates went through a team-matching phase. However, this may no longer be the case; Snap’s recent job postings appear to be for specific roles and teams.

For an idea of what everyday work might look like as a Snap MLE, here are the responsibilities listed for an MLE role on Snap’s Ads Attribution team.

• Building, optimizing, and deploying machine learning models to support identity resolution and ads attribution
• Creating models that help drive value for users, advertisers, and the company
• Collaborating with cross-functional teams to align on machine learning strategies to meet company objectives
• Staying up-to-date with the latest technology in machine learning and applying this knowledge to tackle complex problems
• Performing code reviews and ensuring exceptional code quality
• Building robust, lasting, and scalable products
• Iterating quickly without compromising quality

Recommendations before you apply

• Research Snap’s culture and engineering team. Before you apply for a role with Snap, get to know its mission and values on its careers site. You should also dive into Snap’s Engineering blog for a better idea of what the company’s ML work currently looks like. During your interview, look for opportunities to reference Snap’s latest projects to demonstrate your interest in the company.
• Practice for Snap’s technical rounds. Anonymous candidates describe Snap’s interview process as being “almost purely technical,” with an emphasis on accuracy. This is unlike some other tech companies that pay closer attention to candidates’ critical thinking and behavioral qualities. To that end, it’s important to sharpen your coding and ML coding skills before sending in a job application.
• Hone your communication skills through practice and mock interviews. Strong communication skills are just as important as technical competence. As a highly collaborative company, Snap values candidates who can clearly explain and articulate their thought processes. To practice communicating during technical interviews, consider participating in peer mock interviews or professional interview coaching.
• Get firsthand insights from current MLEs. Find a few Snap MLEs on LinkedIn or Exponent to learn about their education and experience. This can help you build an effective application by understanding what has worked in the past. The Snap MLE we spoke with also encouraged discussing machine learning concepts more broadly with other MLEs to get nuanced perspectives, which can help in preparing for ML-specific interviews.

Interview Process

Snap’s MLE interview loop is highly technical to match the expectations of the role.

The majority of candidates report going through a three-stage interview process:

• An initial recruiter phone screen that typically runs 30-60 minutes
• A 60-minute technical screen to assess your machine learning knowledge and coding skills
• A virtual or in-person interview loop that features 4-6 hourlong rounds about coding, ML fundamentals, and applied ML/ML design
  • For most candidates, there is no dedicated behavioral round. Instead, each interview typically includes 10-15 minutes of behavioral questions.

The interview loop generally takes place over one day but can be split into multiple days if requested.

Many candidates report that Snap’s interview decisions tend to be very quick. However, if Snap’s interviewing committee does not feel they have enough information to make a decision, they may request 1-2 additional interview rounds.

Recruiter and Technical Screens

The first stage of the Snap MLE interview loop involves two preliminary screens: a recruiter screen and a technical screen.

The recruiter screen, which typically runs 30-60 minutes, is a fairly standardized phone conversation.

Expect to go through your resume, talk about why you want to work at Snap, and answer some basic situational questions.

Prepare by practicing introductory questions like the following:

• Tell me about yourself.
• Why do you want to work at Snap?
• Walk me through your resume.

After the recruiter phone screen is a 60-minute technical assessment with an engineer or a hiring manager. Past candidates report spending the first 10-20 minutes on their background and behavioral questions before moving on to difficult data structures and algorithms problems and machine learning questions.

If you pass the technical screen, your recruiter will reach out about the next stage, the onsite loop.

Coding

Snap’s interview loop generally consists of 4-6 rounds, with two dedicated to traditional coding problems. All interviews in this loop are 60 minutes.

A strong understanding of code is integral to working at Snap as MLEs are responsible for not only writing efficient, bug-free code but also reviewing others’ to ensure exceptional code quality.

Most Snap MLE job postings do not require a specific coding language. Instead, a strong general knowledge of software engineering is key. However, Python is an obvious choice for many of the coding challenges.

Snap is looking for MLEs who can use diverse skill sets and strong backgrounds in ML to answer their questions with creativity and accuracy.

Some coding questions of the caliber asked by Snap include:

Machine Learning Fundamentals

Alongside Snap’s coding interviews, expect 1 or 2 additional assessments focused specifically on your machine learning understanding and background.

Depending on the interviewer, you may be asked to present previous ML projects you’ve worked on, or be asked exclusively technical questions.

If you’re asked about previous projects, be prepared to explain the problem statements, data collection, feature engineering, and model selection process involved in your past work. The interviewer may also ask about the model optimization techniques you used and how you dealt with challenges specific to the project.

Expect questions on standard ML topics such as metrics, unbalanced data, overfitting, and optimizers.

Possible questions reported by candidates include:

Applied ML/ML System Design

Beyond machine learning expertise, Snap is looking for candidates who can skillfully apply design principles where ML models are used to solve real-life challenges and situations.

In this ML system design interview, expect questions designed to facilitate creative, fast-paced thinking. You might even be asked design questions specific to Snap’s products. The engineer we spoke with shared that reading Snap’s blog posts proved to be useful for this reason:

“I studied one of their posts on how they designed one of their ad ranking systems. And then I was asked about that in one of my interviews. I probably wouldn’t have done as well if I didn’t study their blog post on it. … In general, familiarity with how big tech companies use machine learning is really helpful.”

For an idea of how the company approaches its product development, take a look at Snap Engineering’s eight key values:

1. Customer Focus: We do what is right for our customer, Snap, and our team, respectively. Positive and successful teams commit to the mission and to each other. Instead of serving themselves, they serve one another.
2. Execution: We focus on the key inputs and deliver them with the right quality and in a timely fashion. We never settle. Ideas are easy, execution is everything.
3. Craftsmanship: The care we put into our work is what delights our customers and makes them love Snap. Fast and good are not mutually exclusive. We are curious and always seek to learn and improve our skills.
4. Creativity: Software engineering is a creative process. While our key metrics are vital in how we manage our business, sometimes the right solution can't be achieved just by optimizing metrics. We take risks and don't let setbacks derail our pursuit of innovation. We learn from our customers, iterate early and often and learn from our mistakes.
5. Accountability: We understand how our work impacts others and we own our mistakes. We write code with the future reader and maintainer in mind and realize a project is not finished when shipped; 80% of execution happens post-launch.
6. Empathy: We assume the most favorable interpretation and give the benefit of doubt, listening and seeking to understand why people have the positions they do in order to come to the best conclusions. When we disagree, we are respectful and humble towards each other. We create a safe and inclusive workplace and embrace different thoughts, people and backgrounds which allows each of us to be uniquely ourselves. Diversity makes us better.
7. Integrity: We are consistent between our words and actions, regardless of the situation or audience. We are not afraid to speak up or disagree. Once the decision is made, we commit to its success fully even if the decision wasn't what we wanted.
8. Audacity: Thinking small is a self-fulfilling prophecy. We create bold objectives that inspire results.

Sample questions to practice include:

Behavioral

According to past candidates, Snap’s machine learning interview generally doesn’t include a dedicated behavioral round.

Instead, each round of the interview loop kicks off with a few behavioral questions (roughly 10-15 minutes) to assess culture fit and candidates’ values before delving into technical questions.

The specifics of the behavioral portion vary, but expect to be asked about:

• Your background: Any experience listed on your resume is fair game.
• Your impact: Specific examples and the resulting impact of previous work.
• Your ability to self-reflect and learn: What did you learn from past experiences? Successes? Failures?
• Your motivations: Why engineering? Why Snap?

To prepare, we recommend creating a bank of stories and experiences that could be relevant to your Snap interview.

Consider choosing anecdotes that reflect or relate to Snap’s three core values:

• We Are Kind: We operate with courage, show empathy, and instill trust through honesty and integrity.
• We Are Smart: We solve problems through action, make high-quality decisions, and think with a strategic mindset.
• We Are Creative: We gracefully manage ambiguity, cultivate innovation, and demonstrate an insatiable desire to learn.

Tips and Strategies

• Master foundational technical concepts. When studying for any technical interview, some candidates take a rote memorization approach. However, simply memorizing coding solutions is a short-term fix that many interviewers can also detect. Instead, seek to understand the basics and improve your logical reasoning so you can apply that knowledge to different questions and situations. The Snap MLE we spoke with received two coding questions they had never seen before, but successfully solved them due to their strong understanding of the fundamentals.
• Emphasize your practical experience. Real-world industry experience is highly sought after at Snap, as can be seen in the required number of years of experience and flexible education needs listed in MLE job postings. During your interviews, make sure to emphasize any relevant projects you’ve worked on in the past.
• Ask for feedback from interviewers. Even if your interviews don’t go how you hoped, they’re a great learning opportunity. Feedback from interviewers can provide valuable insights into areas where you may need to strengthen your knowledge or improve your approach for future interviews. Additionally, by seeking feedback, you demonstrate that you’re open to constructive criticism and genuinely interested in self-improvement.
• Stay in touch with your recruiter. The Snap MLE we spoke with did not get hired by Snap in their first interview attempt. However, after a year, they reached out to the same recruiter they previously worked with. After going through the interview process a second time, they eventually received a hiring offer.

Additional Resources

• Snapchat Engineering Blog
• Machine Learning Engineer Interviews
• Snap Software Engineer Interview Guide
• Top Snap Machine Learning Engineer Interview Questions
• Top Snap Interview Questions
• Top Machine Learning Engineer Interview Questions
• Top Coding Interview Questions

FAQs

Are Snap interviews in person or virtual?

Depending on your prospective role and location, Snap’s onsite interview loop may be virtual or in person. Your recruiter will share additional insights at that point in the process.

Does Snap have an internship program?

Yes! Snap has several opportunities for students and recent grads, including technical internships for software engineer and computer vision engineer roles.