“Included as a consequential modern Indian gaming employer with relevant product, engineering, data or AI roles.”
5
stages
3–8 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
Micron · Taichung - AATT, Taiwan
Senior AI Engineer
Thndr · Remote
Partner Applied AI Engineer, Oracle and Cloud ISV
OpenAI · San Francisco
01
The interview, stage by stage
- 1
Recruiter screen60 min
real-time scalefraud and fairnessPrepare one quantified example demonstrating real-time scale.
- 2
Coding or role specific assessment60 min
fraud and fairnessexperimentationPrepare one quantified example demonstrating fraud and fairness.
- 3
Hiring manager deep dive60 min
experimentationengagementPrepare one quantified example demonstrating experimentation.
- 4
System/product/domain interview60 min
engagementeconomicsPrepare one quantified example demonstrating engagement.
- 5
Behavioral and cross functional panel90 min
economicsreal-time scalePrepare one quantified example demonstrating economics.
02
What decides the offer
Real time scale
25%
Shows specific decisions and measurable evidence for real-time scale.
Fraud and fairness
20%
Shows specific decisions and measurable evidence for fraud and fairness.
Experimentation
20%
Shows specific decisions and measurable evidence for experimentation.
Engagement
20%
Shows specific decisions and measurable evidence for engagement.
Economics
15%
Shows specific decisions and measurable evidence for economics.
They look hardest for real-time scale, fraud and fairness, experimentation, engagement.
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 scale
- ·Practice fraud and fairness
- ·Practice experimentation
Days 6–8
Company-context cases
- ·Detect collusion or fraud in real time.
- ·Design fair matchmaking.
- ·Evaluate an AI feature without harming game integrity.
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 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
- Design a production text-to-speech system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a text-to-speech system without masking regressions?Recommended
- Design a production voice cloning system from data collection through serving.Recommended
- How would you improve the quality, latency, and cost of a voice cloning 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?