“Typical robotics hiring pattern. Expect domain-specific mathematics, robotics architecture and a deep dive into shipped physical systems.”
5
stages
4–9 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
perception and planningsimulationcontrolsPrepare one concrete example and one practice problem for perception and planning.
- 2
Technical fundamentals60 min
simulationcontrolsreal-time systemsPrepare one concrete example and one practice problem for simulation.
- 3
Coding or robotics exercise60 min
controlsreal-time systemsphysical safetyPrepare one concrete example and one practice problem for controls.
- 4
System/research deep dive60 min
perception and planningsimulationcontrolsPrepare one concrete example and one practice problem for real-time systems.
- 5
Onsite team panel240 min
simulationcontrolsreal-time systemsPrepare one concrete example and one practice problem for physical safety.
02
What decides the offer
Perception and planning
25%
Uses specific evidence to demonstrate perception and planning.
Simulation
20%
Uses specific evidence to demonstrate simulation.
Controls
20%
Uses specific evidence to demonstrate controls.
Real time systems
20%
Uses specific evidence to demonstrate real-time systems.
Physical safety
15%
Uses specific evidence to demonstrate physical safety.
They look hardest for perception and planning, simulation, controls, real-time systems.
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 perception and planning
- ·Practice simulation
- ·Practice controls
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
- 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 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
- 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
- What approaches would you use to improve transformer efficiency and performance?Recommended
- How does PyTorch Fully Sharded Data Parallel work?Recommended
- Design a production speech recognition system from data collection through serving.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?