“Typical enterprise agents hiring pattern. A build exercise, workflow critique or customer demo is likely.”
5
stages
2–6 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
Recruiter screen30 min
agent designevaluationtool reliabilityPrepare one concrete example and one practice problem for agent design.
- 2
Product/technical screen60 min
evaluationtool reliabilitycustomer workflowPrepare one concrete example and one practice problem for evaluation.
- 3
Agent build or case60 min
tool reliabilitycustomer workflowsafe automationPrepare one concrete example and one practice problem for tool reliability.
- 4
System design60 min
agent designevaluationtool reliabilityPrepare one concrete example and one practice problem for customer workflow.
- 5
Founder or cross functional panel240 min
evaluationtool reliabilitycustomer workflowPrepare one concrete example and one practice problem for safe automation.
02
What decides the offer
Agent design
25%
Uses specific evidence to demonstrate agent design.
Evaluation
20%
Uses specific evidence to demonstrate evaluation.
Tool reliability
20%
Uses specific evidence to demonstrate tool reliability.
Customer workflow
20%
Uses specific evidence to demonstrate customer workflow.
Safe automation
15%
Uses specific evidence to demonstrate safe automation.
They look hardest for agent design, evaluation, tool reliability, customer workflow.
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 agent design
- ·Practice evaluation
- ·Practice tool reliability
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 would you design multi-tenant retrieval without leaking customer data?Recommended
- Design a batching service for model inference.Recommended
- How would you debug an AI deployment using a customer’s private data?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
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
- Design a model router that balances quality, latency, and cost.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
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?