How Skild AI interviews robot-learning and infra engineers
6 min read How Skild AI, the $14B robot-brain startup, runs its engineering interviews and what each round is really testing. Read article
// ai-ml-interview-questions
ML systems, model design, and the applied-AI loop: RAG, agents, evals, and infrastructure.
6 min read How Skild AI, the $14B robot-brain startup, runs its engineering interviews and what each round is really testing. Read article
7 min read What interviewers probe when they ask you to design a feed: scoping the objective, the retrieval and ranking funnel, two-tower models, cold start, and how you would evaluate it. Read article
7 min read The transformer internals interviewers push on, from why attention scales by sqrt(d_k) to KV cache memory and grouped-query attention. Read article
6 min read Hugging Face screens on public open-source work and an open-ended take-home rather than live algorithm rounds, and the take-home is where offers are decided. Read article
6 min read What AI product teams actually probe when they hand you an eval design question or a broken prompt to fix. Read article
7 min read What GPU and CUDA interviews at Nvidia, Groq, and the inference startups actually test, from warps and coalescing to occupancy and kernel fusion. Read article
6 min read The AI engineer loop stopped looking like a classic ML interview, and the RAG, agent, and evaluation rounds are where candidates get sorted. Read article
7 min read A quick reference for the attention, tokenization, KV-cache, RAG, and evaluation questions that come up in LLM and machine learning interviews. Read article
6 min read A breakdown of how Safe Superintelligence hires, why it vets for character first, and what its research interview really probes. Read article
7 min read Thinking Machines Lab hires a few dozen people at a $12 billion valuation, so its interview screens hard for depth in one area over broad competence. Read article
7 min read How Groq structures its engineering loop and the questions it uses to test whether you actually understand deterministic, compile-time inference. Read article
6 min read The GenAI engineer loop looks like a machine-learning loop until you hit the retrieval design and evaluation rounds, where most candidates quietly fall apart. Read article
7 min read A walkthrough of the drift, training-serving skew, feature-store, and rollout questions MLOps interviewers use to tell production experience from theory. Read article
6 min read A walkthrough of the two-stage recommender this round almost always comes down to, and what interviewers push on once you’ve drawn it. Read article
7 min read A walk through the HNSW and IVF questions vector search interviews actually ask, and the recall-versus-latency reasoning behind them. Read article
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