“Typical big tech hiring pattern. Structured coding, design, behavioral and role-domain rounds; team-specific AI depth is added on top.”
5
stages
4–8 weeks
end to end
23
practice questions
01
The interview, stage by stage
- 1
Application and recruiter screen30 min
coding fundamentalsML system designproduct judgmentPrepare one concrete example and one practice problem for coding fundamentals.
- 2
Online assessment or technical screen60 min
ML system designproduct judgmentbehavioral competenciesPrepare one concrete example and one practice problem for ML system design.
- 3
Hiring manager conversation60 min
product judgmentbehavioral competenciescross-functional scalePrepare one concrete example and one practice problem for product judgment.
- 4
Four to six interview loop240 min
coding fundamentalsML system designproduct judgmentPrepare one concrete example and one practice problem for behavioral competencies.
- 5
Team matching or offer review60 min
ML system designproduct judgmentbehavioral competenciesPrepare one concrete example and one practice problem for cross-functional scale.
02
What decides the offer
Coding fundamentals
25%
Uses specific evidence to demonstrate coding fundamentals.
ML system design
20%
Uses specific evidence to demonstrate ML system design.
Product judgment
20%
Uses specific evidence to demonstrate product judgment.
Behavioral competencies
20%
Uses specific evidence to demonstrate behavioral competencies.
Cross functional scale
15%
Uses specific evidence to demonstrate cross-functional scale.
They look hardest for coding fundamentals, ML system design, product judgment, behavioral competencies.
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 coding fundamentals
- ·Practice ML system design
- ·Practice product judgment
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
- Explain the trade-offs involved in model selection.Reported
- Tell me about a time you handled bias in an AI product.Reported
- Walk me through the system design of an AI-powered user experience.Reported
- How would you improve Microsoft Copilot?Reported
- How would you measure the success of a generative-AI 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
- Offline evaluations improved but production metrics fell. What happened?Recommended
- Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.Recommended
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
- How would you audit whether an AI system treats demographic groups fairly?Recommended
- How would you build a feedback loop without amplifying user bias or abuse?Recommended
- Design an AI coding assistant for a large enterprise codebase.Recommended
- Design memory for a long-running personal AI assistant.Recommended
- A model refuses too often after a safety update. How do you diagnose and fix it?Recommended
- Design a language model that minimizes harmful outputs while remaining useful and expressive.Recommended
- Represent a one-nearest-neighbor classifier using a feed-forward neural network.Recommended
- Design a machine-learning system.Recommended
- What approaches would you use to improve transformer efficiency and performance?Recommended
- Design a production recommendation systems system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a recommendation systems system without masking regressions?Recommended
- Design a production search ranking system from data collection through serving.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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