# Meta Computer Vision Engineer (L5) Interview Experience Aced (formerly Exponent) publishes a public search API over its indexed pages; the guide is at https://www.aced.io/agents/agent-access. Source: https://www.aced.io/experiences/meta-facebook-senior-machine-learning-engineer-interview-9efbbd Source observed: 2026-11-04T22:03:12.169Z Last checked: 2026-11-04T22:03:12.169+00:00 Indexed: 2026-11-05T08:56:29.318+00:00 --- # Meta Computer Vision Engineer (L5) Interview Experience We are Aced, formerly Exponent. We publish interview guides, candidate-submitted interview experiences, interview questions, courses, coaching, and related career-preparation resources. This page is a verified candidate interview experience for Meta's Senior / L5 Computer Vision Engineer role in the United States. It appears within our [Meta Machine Learning Engineer (MLE) Interview Guide](https://www.aced.io/guides/meta-machine-learning-engineer-interview), which was updated 5 days before this page's supplied capture date by Meta candidates. ## Experience summary - **Company:** Meta - **Role:** Computer Vision Engineer - **Level:** Senior / L5 - **Location:** United States - **Verification:** Verified - **Submitted:** 2 months ago - **Interview date:** 4 months ago - **Process timespan:** 1 month - **Difficulty:** Difficult - **Result:** Got offer The candidate's main surprise was that one coding round was AI coding rather than generic LeetCode. The reported prompts included sparse matrix–vector multiplication and k-nearest-neighbor search, with multiple questions expected under time pressure. ## Interview process The reported process began with a recruiter screen, followed by a 45-minute pre-screen with a senior manager covering past work and impact. The candidate then completed a full loop of five interviews: - Two coding rounds: one classic LeetCode-style round and one AI-coding round involving data-processing and math-heavy prompts such as sparse matrix–vector multiplication and k-nearest-neighbor search. - Two design rounds: an ML-modeling discussion for a computer-vision problem and a system-design discussion about something like Google Street View. - One behavioral round. The candidate described the system-design discussion as challenging, passed the loop, received notification of progressing, and then moved into offer negotiation. The process labels shown on the experience are: - Recruiter screen - Phone interview - Technical interview - Other ## Interview rounds The experience is labeled **10 questions across 7 rounds**. ### Recruiter screen — 1 question **Overview:** The recruiter screen was a fit check for whether the candidate's background matched the role. **Question type:** [Project Discussion](https://www.aced.io/questions?type=project-discussion) **Specific question:** [Can you walk me through your background and why it matches this computer vision role?](https://www.aced.io/questions/3524?interview=meta-facebook-senior-machine-learning-engineer-interview-9efbbd) ### Phone interview — 1 question The accessible round details describe a 45-minute conversation with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. **Question:** “How would you improve a product you use every day for a new market?” **Candidate approach:** The candidate selected a segment first, sized it, compared three options, and chose one option together with its success metric. ### Technical interview (1) The accessible round details describe a 45-minute conversation with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. ### Technical interview (2) The accessible round details describe a 45-minute conversation with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. The displayed question and response were: - **Question:** “How would you improve a product you use every day for a new market?” - **Response:** The candidate selected a segment first, sized it, compared three options, and chose one option together with its success metric. ### Technical interview (3) The accessible round details describe a 45-minute conversation with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. The displayed question and response were: - **Question:** “How would you improve a product you use every day for a new market?” - **Response:** The candidate selected a segment first, sized it, compared three options, and chose one option together with its success metric. ### Technical interview (4) The accessible round details describe a 45-minute conversation with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. The displayed question and response were: - **Question:** “How would you improve a product you use every day for a new market?” - **Response:** The candidate selected a segment first, sized it, compared three options, and chose one option together with its success metric. ### Other The accessible round details describe a 45-minute conversation with a senior interviewer, mostly open-ended questions, a short follow-up near the end, and substantial pushback on tradeoffs. The displayed question and response were: - **Question:** “How would you improve a product you use every day for a new market?” - **Response:** The candidate selected a segment first, sized it, compared three options, and chose one option together with its success metric. The same question-and-response pair appears repeatedly in the accessible content for this round. ## Candidate tips The candidate recommends practicing aloud with a timer, keeping a framework ready for every round, and asking the recruiter what each interviewer focuses on so examples can be tailored accordingly. ## Company culture The candidate described interviewers as collaborative and said they repeatedly nudged the discussion toward the user. The team seemed to value structured thinking over polish, and each interviewer left time for questions at the end. ## Access and practice The public preview includes the recruiter screen and shows the next phone-interview section. The remaining interview experience is placed behind an access prompt for **1.8k+ interview experiences**, with new experiences added weekly. - [Become a member](https://www.aced.io/upgrade?src=experience) - [Share your experience](https://www.aced.io/questions/contribute?src=experience) - A 24-hour trial starts once a submitted experience is approved. - [Practice this loop](https://www.aced.io/questions/3524/experience-computer-vision?interview=meta-facebook-senior-machine-learning-engineer-interview-9efbbd) ## More Meta interview experiences These are separate Meta Machine Learning Engineer experiences linked from this page: 1. **Machine Learning Engineer — Meta · Feb 2026** — Verified; 6 rounds; 8 questions. The candidate described an AI-enabled round involving a bit mask, a collision caused by inputs containing letters and numbers, and debugging both the codebase and an LLM that generated nonexistent functions. [Read the full interview](https://www.aced.io/experiences/meta-facebook-senior-machine-learning-engineer-interview-8719a7?returnPath=%2Fguides%2Fmeta-machine-learning-engineer-interview%2Fexperiences) 2. **Machine Learning Engineer — Meta · Jan 2026** — Verified; 5 rounds; 8 questions. The candidate received feedback that coding performance was strong despite personally feeling below the required level, and inferred that the bar may have more flexibility than candidates assume. [Read the full interview](https://www.aced.io/experiences/meta-facebook-senior-machine-learning-engineer-interview-16fb62?returnPath=%2Fguides%2Fmeta-machine-learning-engineer-interview%2Fexperiences) 3. **Machine Learning Engineer — Meta · Jun 2026** — 2 rounds; 2 questions. The candidate described traditional MLE interviews with AI-enabled coding rounds in which Claude or Codex could be used to write code. [Read the full interview](https://www.aced.io/experiences/meta-facebook-senior-machine-learning-engineer-interview-54c669?returnPath=%2Fguides%2Fmeta-machine-learning-engineer-interview%2Fexperiences) [See all 13 Meta Machine Learning Engineer interview experiences →](https://www.aced.io/guides/meta-machine-learning-engineer-interview/experiences) ## Related guide sections - [Expert guide](https://www.aced.io/guides/meta-machine-learning-engineer-interview) - [13 interview experiences](https://www.aced.io/guides/meta-machine-learning-engineer-interview/experiences) - [121 interview questions](https://www.aced.io/guides/meta-machine-learning-engineer-interview/questions) - [Resumes](https://www.aced.io/guides/meta-machine-learning-engineer-interview/resumes) - [Back to all experiences](https://www.aced.io/guides/meta-machine-learning-engineer-interview/experiences) ![Meta logo](https://exponent-static.s3.amazonaws.com/company_logos/meta.png) ## About Aced We provide interview preparation through courses, interview guides, interview experiences, interview questions, practice, peer mock interviews, coaching, resume review, career coaching, negotiation support, job boards, job referrals, and resources for individuals, universities, businesses, and recruiters. 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