“Typical frontier lab hiring pattern. Research presentation or role-specific technical exercise is common; exact format varies sharply by team.”
6
stages
4–10 weeks
end to end
18
practice questions
01
The interview, stage by stage
- 1
Recruiter or talent screen30 min
research depthML codingresearch tastePrepare one concrete example and one practice problem for research depth.
- 2
Hiring manager/research fit60 min
ML codingresearch tastesystems thinkingPrepare one concrete example and one practice problem for ML coding.
- 3
Coding or technical screen60 min
research tastesystems thinkingmission alignmentPrepare one concrete example and one practice problem for research taste.
- 4
Research or system design deep dive60 min
research depthML codingresearch tastePrepare one concrete example and one practice problem for systems thinking.
- 5
Multi interviewer final loop240 min
ML codingresearch tastesystems thinkingPrepare one concrete example and one practice problem for mission alignment.
- 6
References and decision60 min
research tastesystems thinkingmission alignmentPrepare one concrete example and one practice problem for research depth.
02
What decides the offer
Research depth
25%
Uses specific evidence to demonstrate research depth.
ML coding
20%
Uses specific evidence to demonstrate ML coding.
Research taste
20%
Uses specific evidence to demonstrate research taste.
Systems thinking
20%
Uses specific evidence to demonstrate systems thinking.
Mission alignment
15%
Uses specific evidence to demonstrate mission alignment.
They look hardest for research depth, ML coding, research taste, systems thinking.
03
Your four weeks
Week 1
Company, product and role model
- ·Read current product/research material
- ·Map the role to three company problems
- ·Prepare a two-minute motivation narrative
Week 2
Core technical and product competencies
- ·Practice research depth
- ·Practice ML coding
- ·Practice research taste
Week 3
Timed simulations
- ·Complete two timed exercises
- ·Run one system/product design mock
- ·Refine six behavioral stories
Week 4
Company-specific loop rehearsal
- ·Practice linked questions
- ·Rehearse project deep dive with adversarial follow-ups
- ·Prepare interviewer questions and logistics
04
Practice these
- How would you design an AI assistant for doctors?Recommended
- How would you launch an AI feature whose output cannot always be objectively graded?Recommended
- Design a production RAG system for ten million documents.Recommended
- How would you defend an LLM application against prompt injection?Recommended
- Design an agent that can safely call external tools.Recommended
- Offline evaluations improved but production metrics fell. What happened?Recommended
- How do you evaluate outputs when human raters disagree?Recommended
- How would you test an AI system for rare but severe failures?Recommended
- Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.Recommended
- Design an ablation study for a new agent architecture.Recommended
- How would you design multi-tenant retrieval without leaking customer data?Recommended
- Design a batching service for model inference.Recommended
- Design an account-research and personalization pipeline for 10,000 prospects.Recommended
- Design an enterprise architecture for using multiple model providers.Recommended
- How do you investigate a production incident involving unsafe model output?Recommended
- Design a red-team program for a new general-purpose model.Recommended
- How would you measure jailbreak resistance without overfitting to known attacks?Recommended
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
05
Where people slip
- !Generic motivation that could apply to any AI company
- !Buzzword-heavy answers without mechanisms
- !No measurable impact or personal ownership
- !Ignoring cost, latency, safety or operational constraints
- !Treating reported questions as a script rather than preparing underlying skills
06
Ask them this
- ?What distinguishes strong performance in the first six months?
- ?Which model, data or product constraint most limits the team today?
- ?How are research, product and engineering decisions resolved?
- ?How does the team evaluate AI quality before and after launch?
- ?What is the policy on AI-tool use during each interview stage?
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