“Typical ai hardware hiring pattern. Expect detailed project interrogation and performance reasoning rather than only generic algorithms.”
5
stages
3–8 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
GPU/accelerator architecturedistributed systemsperformance analysisPrepare one concrete example and one practice problem for GPU/accelerator architecture.
- 2
Technical background deep dive60 min
distributed systemsperformance analysisML fundamentalsPrepare one concrete example and one practice problem for distributed systems.
- 3
Coding or low level systems screen60 min
performance analysisML fundamentalshardware-software co-designPrepare one concrete example and one practice problem for performance analysis.
- 4
Hardware aware ML design60 min
GPU/accelerator architecturedistributed systemsperformance analysisPrepare one concrete example and one practice problem for ML fundamentals.
- 5
Team panel240 min
distributed systemsperformance analysisML fundamentalsPrepare one concrete example and one practice problem for hardware-software co-design.
02
What decides the offer
GPU/accelerator architecture
25%
Uses specific evidence to demonstrate GPU/accelerator architecture.
Distributed systems
20%
Uses specific evidence to demonstrate distributed systems.
Performance analysis
20%
Uses specific evidence to demonstrate performance analysis.
ML fundamentals
20%
Uses specific evidence to demonstrate ML fundamentals.
Hardware software co design
15%
Uses specific evidence to demonstrate hardware-software co-design.
They look hardest for GPU/accelerator architecture, distributed systems, performance analysis, ML fundamentals.
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 GPU/accelerator architecture
- ·Practice distributed systems
- ·Practice performance analysis
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 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
- Design a batching service for model inference.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
- How would you serve a model under a strict p99 latency SLO?Recommended
- How would you capacity-plan an inference service with bursty traffic?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
- Design a text-to-video generation system.Recommended
- What approaches would you use to improve transformer efficiency and performance?Recommended
- Explain distributed training and connect it to your past projects.Recommended
- How does PyTorch Fully Sharded Data Parallel work?Recommended
- Explain how CPU and GPU architecture affects deep-learning performance.Recommended
- Design a production speech recognition system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a speech recognition system without masking regressions?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?