“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
25
practice questions
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landed applies to roles like these for you, on each company’s own site, with answers written for the role.
Customer Marketing Lead, Startups
San Francisco, CA
Partner Support Operations Lead
San Francisco, CA
Tech Lead Manager, Growth Data Science
San Francisco, CA
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
- What generative-AI skills will be most critical in the next year?Reported
- Define hallucinations in LLMs and explain how you would reduce them.Reported
- How do you approach generative-AI safety in a consumer product?Reported
- 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
- Write code rapidly to a specification, then refactor it as new constraints are introduced.Reported
- When is a prompt cache calculated, and what is a model harness?Reported
- Evaluate LLMs on a toy task and use LLMs to generate additional evaluation data.Reported
- Design a language model that minimizes harmful outputs while remaining useful and expressive.Reported
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?