Meta
Llama models, Meta AI assistant and large-scale recommendation systems
Interview in a few hours? A quick sprint for Meta
AI in interviews
Select technical interviews include a built-in AI assistant in CoderPad (choice of Claude, ChatGPT, Gemini or Meta models) that candidates are expected to use. Any outside AI tool is not authorized.
Typical interview loop
How Meta usually runs it for applied AI and LLM engineer roles.
- Focus: Background & level match
Recruiter screen
A call on your background, level and preferences, plus the details of the loop you'll face.
- Focus: Speed & algorithmic precision
Technical screen
About 45 minutes in CoderPad solving two medium-difficulty algorithm problems under time pressure.
- Focus: DSA without AI
Onsite: classic coding
A traditional algorithms round with no AI assistant, judged on optimal time and space complexity.
- Focus: Verification, debugging & code reading
Onsite: AI-enabled coding
A 60-minute round in a multi-file codebase with a built-in AI assistant: fix bugs, add features, then optimize, checking everything the AI suggests.
- Focus: End-to-end ML design
Onsite: ML system design
Designing a production ML service end to end; since 2026 often focused on a sub-domain such as GenAI/LLM design, with recommenders less common.
- Focus: Impact & collaboration
Onsite: behavioral
Several questions with follow-ups on past work, conflict and impact in an unstructured, fast-moving setting.
How it weights your plan
Selecting Meta when you build a plan multiplies each topic’s priority score:
Official prep and resources
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