“Included as a consequential modern Indian robotics 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
perception and planningcontrolsPrepare one quantified example demonstrating perception and planning.
- 2
Practical role exercise60 min
controlsreal-time softwarePrepare one quantified example demonstrating controls.
- 3
Technical/product deep dive60 min
real-time softwaresimulationPrepare one quantified example demonstrating real-time software.
- 4
Team values and ownership round90 min
simulationphysical safetyPrepare one quantified example demonstrating simulation.
02
What decides the offer
Perception and planning
25%
Shows specific decisions and measurable evidence for perception and planning.
Controls
20%
Shows specific decisions and measurable evidence for controls.
Real time software
20%
Shows specific decisions and measurable evidence for real-time software.
Simulation
20%
Shows specific decisions and measurable evidence for simulation.
Physical safety
15%
Shows specific decisions and measurable evidence for physical safety.
They look hardest for perception and planning, controls, real-time software, simulation.
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 perception and planning
- ·Practice controls
- ·Practice real-time software
Days 6–8
Company-context cases
- ·Design autonomous navigation in a warehouse.
- ·Debug sim-to-real performance gaps.
- ·Set safety fallback and recovery behavior.
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
- 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
- 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
- How would you improve the quality, latency, and cost of a search ranking system without masking regressions?Recommended
- Design a production fraud detection system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a fraud detection system without masking regressions?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?