“Included as a consequential modern Indian ai evaluation employer with relevant product, engineering, data or AI roles.”
4
stages
2–5 weeks
end to end
20
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
Founder or hiring manager screen60 min
evaluation designhuman judgmentPrepare one quantified example demonstrating evaluation design.
- 2
Practical role exercise60 min
human judgmentobservabilityPrepare one quantified example demonstrating human judgment.
- 3
Technical/product deep dive60 min
observabilityregression testingPrepare one quantified example demonstrating observability.
- 4
Team values and ownership round90 min
regression testingproduct outcomesPrepare one quantified example demonstrating regression testing.
02
What decides the offer
Evaluation design
25%
Shows specific decisions and measurable evidence for evaluation design.
Human judgment
20%
Shows specific decisions and measurable evidence for human judgment.
Observability
20%
Shows specific decisions and measurable evidence for observability.
Regression testing
20%
Shows specific decisions and measurable evidence for regression testing.
Product outcomes
15%
Shows specific decisions and measurable evidence for product outcomes.
They look hardest for evaluation design, human judgment, observability, regression testing.
03
Your four weeks
Days 1–2
Company and market
- ·Map products, users, revenue model and competitors
- ·Write a one-page company thesis
Days 3–5
Core role skills
- ·Practice evaluation design
- ·Practice human judgment
- ·Practice observability
Days 6–8
Company-context cases
- ·Design a representative golden dataset.
- ·Resolve disagreement among human raters.
- ·Set release thresholds under uncertainty.
Days 9–11
Technical simulations
- ·Complete two timed exercises
- ·Run one architecture/product mock
- ·Practice follow-up pressure
Days 12–14
Stories and final loop
- ·Prepare six quantified ownership stories
- ·Rehearse project deep dive
- ·Prepare interviewer questions
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
- How would you detect memorization or sensitive-data leakage from a model?Recommended
- Design a model router that balances quality, latency, and cost.Recommended
05
Where people slip
- !Generic enthusiasm about startup culture or AI
- !No understanding of the company business model
- !Ignoring Indian price sensitivity, regulation or operational variance
- !Architecture without failure recovery and observability
- !Claiming a recommended case was actually asked
06
Ask them this
- ?What is the exact loop for this team and level?
- ?Which rounds permit AI tools?
- ?What would I own in the first 90 days?
- ?What is the hardest product or model constraint the team faces in India?
- ?How does the team measure quality after launch?
- ?How are ESOPs valued and what is the exercise policy?