“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
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?
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