Frontier AI / foundation models AI Engineer Interview Loop
There's no standard AI engineer interview yet. At most companies it's a software or ML engineer loop with retrieval, serving and agent questions added. A few things keep coming up. The screen often asks how you actually use AI tools. Coding is still algorithmic, but more and more it's project-style or multi-level. There's LLM-specific coding, like sampling, decoding, a tokenizer or an attention mask, and code review, refactoring or debugging. You'll also get AI system design under a latency or token budget, LLM concepts, and a project deep dive with a values conversation. Rules on AI tools differ. Mistral runs a coding round where AI is allowed next to an AI-free code review, while Anthropic bans AI in live rounds.
Practice by round
Easy LLM coding and quick concepts: softmax temperature, top-k cosine similarity search, int8 quantization, and padding sequences with attention masks.
Practical algorithm problems, like an LRU cache for model responses, a token-bucket rate limiter, and batching requests under a token budget. Next to them is LLM coding: top-k and top-p filtering, greedy decoding with a repetition penalty, and a KV cache. Practice in debug mode for the code review round.
AI system design, like inference batching, serving under a latency budget, the faster-versus-better model trade-off, and guardrails for an agent. Then there's RAG Answer Support, a mini-analysis on choosing what a model reads under a token budget.
| Case study | Difficulty |
|---|---|
| Shadow Deployment & Canary Rollout | Medium |
| A/B Testing Model Versions | Medium |
| Model Registry & Rollback | Medium |
| Recommendation Serving Under Cold-Start | Medium |
| Faster vs. Better Model Trade-off | Medium |
| Guardrails for an Autonomous Agent | Hard |
| Data project | Difficulty |
|---|---|
| RAG Answer Support | Medium |
LLM concepts for building products: a RAG token budget, managing the context window, controlling cost, LLM-as-judge bias and prompt injection.