“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
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?
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