Meta Machine Learning Engineer
interview guide
Prepare for coding, ML design and technical project discussions, with clear distinctions between numerical ML tasks and coding with an AI assistant.
What to expect at Meta
Meta MLE interview routes can combine conventional algorithms with recommendation-system design, repository debugging or numerical implementation. Computer-vision candidates also describe separate modelling and production-system discussions. The guide explains these differences so preparation for an 'AI coding' round starts with the actual task and tool permissions.
General-pool, L5 and computer-vision routes use different combinations of interviews. Ask which coding and design formats apply to your team before choosing where to spend your time.
Preparing for recommendation-system design
A recommendation design needs a clear definition of a useful result. Recommending places in an unfamiliar city and ranking a video feed involve different user needs and available signals. Explain how the chosen objective affects the candidates you retrieve, the data used for ranking and the evidence you would collect to judge the result.
Prepare to connect an offline comparison to a live product decision. State what the held-out data can establish, then explain what an online experiment still needs to show. The guide distinguishes versions that emphasise algorithm and evaluation from those that probe data representation, so follow the depth of the actual question instead of forcing every case through the same feature list.
Inside the full guide
8 sectionsInterview routes and the written pre-screen
General-pool, L5 and computer-vision sequences, plus what to clarify with your recruiter.
Conventional coding
Algorithm questions where input structure and exact return requirements matter.
AI-assisted repository debugging
Finding failures, checking generated suggestions and explaining your final change.
Maze solving and friend recommendations
Implementation tasks with follow-ups on evaluation and test data.
ML coding for computer vision
Sparse operations, nearest-neighbour search and explaining numerical implementations.
ML design across domains
Recommendations, content detection and the distinction between modelling and production systems.
Behavioural and project depth
Personal ownership, measured impact, disagreements and delivery under pressure.
How to allocate preparation time
Combining coding, domain-specific design and project rehearsal for the confirmed loop.