“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
18
practice questions
Hiring now
AI engineer roles are open right now
landed applies to roles like these for you, on each company’s own site, with answers written for the role.
AI Engineer
InMobi · San Mateo, CA
Staff AI Engineer
Coupang · Seattle, USA
AI/ML Engineer
Kaizen Labs · New York, NY
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
- 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?