Meta AI Infrastructure System Design interview questions
Practise ai infrastructure system design for Meta AI Infrastructure Engineer roles. Design an inference platform at 10k requests per second, a 10,000-GPU training cluster, a job scheduler with preemption and checkpointing, a serverless GPU runtime with sub-second cold starts, a multi-tenant fine-tuning service, an eval pipeline. The whiteboard round at OpenAI, Anthropic, Baseten and Together. Questions tagged to Meta appear first, followed by additional practice from our AI Infrastructure System Design track. Company tags indicate preparation relevance, not confirmed interview appearances. Confirm the current interview format with your recruiter.
PREPARATION CONTEXT · LeetCode-medium coding in 45 minutes without execution, plus the practical AI-enabled round. Sources and confidence in the Meta guide →
AI Infrastructure System Design questions tagged to Meta
Additional AI Infrastructure System Design practice
These questions cover the same topic and are not tagged to Meta. Use them to practise applying the ideas in a different situation.
Concepts for AI Infrastructure System Design preparation
These concept explanations cover the ideas used in the questions. Check each card for its access level.
This collection includes practice questions such as "Design a training cluster for 10,000 GPUs: pods, fabric, storage, failure domains and the scheduler. Justify every number.", "Design the pipeline that produces 15 trillion training tokens: ingest, dedup, tokenize, shard, serve. Throughput per stage.", "Design the checkpoint store for a lab running several large training jobs: write bursts, retention, resharding and lineage.". Company tags describe relevance to preparation; they do not confirm that Meta asked these questions. Check each question for answer access.
Start with a question at your level, use the linked concepts to understand the mechanism, then practise explaining your decision and its limits. Review the company guide's sources and confidence notes, and confirm the current role and interview format with your recruiter.
Yes: Software Engineer, Systems ML (E4 to E5 postings in 2026 covering AI infra, hardware acceleration and benchmarks), Research Engineer, Production Engineer for AI infrastructure, and infrastructure roles inside Meta Superintelligence Labs.
Documented SWE and MLE loop: a recruiter screen, a 45-minute coding screen, then up to six 45-minute rounds: two coding (one of which may be the AI-enabled round), ML system design and sometimes infra design, and behavioral. Production Engineer loops (first-hand 2025 debrief): a screen with a file-parsing task, an easy string problem and a troubleshooting exercise; an onsite of coding with optimization follow-ups, PE system design, a Linux round (memory, processes, filesystems, security, networking) and behavioral.
Other topics for Meta preparation
Explore the other topics mapped to Meta in our preparation guide.
Continue your Meta interview preparation
Continue with related questions and the concept curriculum. Free questions are available in every track; check pricing for full access.
Independent and not affiliated with Meta. All trademarks belong to their owners.
