“Included as a consequential modern Indian quick commerce 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
real-time systemsinventory and forecastingPrepare one quantified example demonstrating real-time systems.
- 2
Practical role exercise60 min
inventory and forecastingroutingPrepare one quantified example demonstrating inventory and forecasting.
- 3
Technical/product deep dive60 min
routingunit economicsPrepare one quantified example demonstrating routing.
- 4
Team values and ownership round90 min
unit economicsoperationsPrepare one quantified example demonstrating unit economics.
02
What decides the offer
Real time systems
25%
Shows specific decisions and measurable evidence for real-time systems.
Inventory and forecasting
20%
Shows specific decisions and measurable evidence for inventory and forecasting.
Routing
20%
Shows specific decisions and measurable evidence for routing.
Unit economics
20%
Shows specific decisions and measurable evidence for unit economics.
Operations
15%
Shows specific decisions and measurable evidence for operations.
They look hardest for real-time systems, inventory and forecasting, routing, unit economics.
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 real-time systems
- ·Practice inventory and forecasting
- ·Practice routing
Days 6–8
Company-context cases
- ·Design inventory allocation across dark stores.
- ·Predict delivery ETA under rapidly changing supply.
- ·Reduce substitutions without increasing waste.
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
- Offline evaluations improved but production metrics fell. What happened?Recommended
- How do you evaluate outputs when human raters disagree?Recommended
- Compare supervised fine-tuning, preference optimization, and reinforcement learning from feedback.Recommended
- Design an ablation study for a new agent architecture.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 detect memorization or sensitive-data leakage from a model?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
- Evaluate LLMs on a toy task and use LLMs to generate additional evaluation data.Recommended
- Design a language model that minimizes harmful outputs while remaining useful and expressive.Recommended
- Assign human labelers, tasks, and models so every pairing is balanced.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
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?