Meta Computer Vision Engineer (L5) Interview Experience
Meta · Senior · Machine Learning Engineer
What caught me off guard was that one of the coding rounds was not generic LeetCode at all. It was AI coding, with things like sparse matrix times vector multiplication and k-nearest neighbor search, and they still wanted multiple questions under pressure.
Interview process
What surprised me most was how much the process emphasized solving several coding questions under pressure. I had an initial recruiter screen, then a 45-minute pre-screen with a senior manager about my past work and impact, and after that a full loop of five interviews. The loop was two coding rounds, one of them classic LeetCode-style and one of them AI coding with data-processing and math-heavy prompts like sparse matrix times vector multiplication and k-nearest neighbor search, plus two design rounds and one behavioral. The design interviews were split between an ML modeling discussion for a computer vision problem and a system design round on something like Google Street View, which I found pretty challenging. I passed the loop, got notified that I made it through, and then moved into offer negotiation.
Interview rounds · 7
- 1
Recruiter screen
Project DiscussionI started with a recruiter screen that was basically a fit check for whether my background matched the role.
- Q1. Can you walk me through your background and why it matches this computer vision role?
- 2
Phone screen
Project DiscussionBehavioralAfter that I had a 45-minute pre-screen with a senior manager, and it was very focused on my past work, what I had actually delivered, and what impact it had.
Q1. Walk me through your experience and what you delivered in your previous projects.
How they answeredI went through my past experience project by project and focused on what I had delivered and the impact. The tone of that round was very much about whether I had actually done the work and could speak concretely about it, not just give a high-level summary.
Follow-up questions- What exactly did you do on each project?
- What was the impact of the things you did?
- 3
Technical round
CodingData Structures & AlgorithmsOne of the coding rounds felt like a more classic LeetCode-style interview. The pressure point was the pacing, because they told me upfront we might do two or three questions depending on time.
- 4
Technical round
CodingMachine LearningThe other coding round was the most distinctive part for me because it was not generic LeetCode. It was what they called AI coding, so the questions were more about data processing and math that felt typical of AI work, and they were still pushing for two or three problems under time pressure.
Q1. Implement an efficient sparse matrix times vector multiplication.
How they answeredI treated it like an AI-flavored coding problem rather than a standard data structures one. The notable part was that I had to code it efficiently under pressure, knowing they wanted multiple questions in the same round.
Q2. Implement a k-nearest neighbor search.
How they answeredAgain, I approached it as a data-processing and math-heavy coding prompt. What stood out was less the exact algorithm and more that this round clearly tested whether I could handle AI-style coding fast, not just do generic interview problems.
Q3. What's the general idea for the third question?
How they answeredIn one of the coding interviews, they were pushing on pace enough that for the third question the interviewer just asked me for the general idea instead of having me fully code it. That gives you a sense of how compressed and speed-focused these rounds were.
- 5
Technical round
Machine LearningArtificial IntelligenceSystem DesignOne design round was really a computer vision and ML modeling discussion, not a traditional system design. High-level: discussion centered on depth from a single image and involved real back-and-forth on my approach.
Q1. How would you approach depth from a single image?
How they answeredI walked through my idea and then went back and forth with the interviewer on it. It was a modeling discussion with nuance and pushback rather than a generic open-ended design chat.
Follow-up questions- Can you go deeper on the nuances and trade-offs in your modeling idea?
- 6
Technical round
System DesignMachine LearningThe second design round was a true system design interview and I found it challenging to crack. The prompt was to design something like Google Street View, and the hard part was balancing the algorithm side, the collection side, and how the whole thing would be handled in production.
Q1. How would you design a system like Google Street View?
How they answeredI had to think about it as more than just the algorithm. I tried to balance the algorithmic piece with data collection and production handling, and that was what made the round challenging because they were clearly looking for a full end-to-end design, not just one strong technical slice.
Follow-up questions- How would you think about the collection side?
- How would you handle it in production?
- 7
Other round
BehavioralThere was also one standard behavioral round. It was the usual big-tech style set of stories around pressure, conflict, and growth after tough feedback.
- Q1. Tell me about a time you were under schedule pressure to deliver something you had committed to.
- Q2. Tell me about a time you had a conflict with a member of your team. What was the conflict and how did you handle it?
- Q3. Tell me about a time you got a less than stellar review from your manager. How did you handle it and how did you grow from it?
Tips from the candidate
Generic LeetCode will NOT cut it. I would practice doing two or three coding problems back-to-back under time pressure, and I would make sure some of that prep is AI-flavored coding around data processing and math, not just classic algorithms. I would also be ready for two very different kinds of design interviews: one that is really ML modeling for computer vision, and one that is true end-to-end system design. For behavioral, I would have clean stories ready on delivery pressure, conflict, and responding to tough feedback.
Company culture
The process felt pretty standard for a big tech company, but with a heavier emphasis on fast live coding than I expected. The interviewers were also pretty deliberate about pacing and scope, so it did not feel like casual conversation rounds. It felt structured, high-bar, and very intentional about covering both modeling depth and production thinking.