“Typical data platform hiring pattern. Expect data modeling, pipelines, ML deployment and enterprise trade-offs.”
5
stages
3–8 weeks
end to end
18
practice questions
Hiring now
H2O.ai has 15 open roles right now
landed applies to roles like these for you, on each company’s own site, with answers written for the role.
Senior AI Engineer
Remote
Senior Manager, AI Engineering
Sydney
Manager, AI Engineering
Remote
01
The interview, stage by stage
- 1
Recruiter screen30 min
data systemsML lifecycleSQL and codingPrepare one concrete example and one practice problem for data systems.
- 2
Coding/data assessment60 min
ML lifecycleSQL and codingenterprise architecturePrepare one concrete example and one practice problem for ML lifecycle.
- 3
System or ML design60 min
SQL and codingenterprise architecturecustomer outcomesPrepare one concrete example and one practice problem for SQL and coding.
- 4
Product/customer case60 min
data systemsML lifecycleSQL and codingPrepare one concrete example and one practice problem for enterprise architecture.
- 5
Behavioral final loop240 min
ML lifecycleSQL and codingenterprise architecturePrepare one concrete example and one practice problem for customer outcomes.
02
What decides the offer
Data systems
25%
Uses specific evidence to demonstrate data systems.
ML lifecycle
20%
Uses specific evidence to demonstrate ML lifecycle.
SQL and coding
20%
Uses specific evidence to demonstrate SQL and coding.
Enterprise architecture
20%
Uses specific evidence to demonstrate enterprise architecture.
Customer outcomes
15%
Uses specific evidence to demonstrate customer outcomes.
They look hardest for data systems, ML lifecycle, SQL and coding, enterprise architecture.
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 data systems
- ·Practice ML lifecycle
- ·Practice SQL and coding
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
- 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 would you design multi-tenant retrieval without leaking customer data?Recommended
- How would you debug an AI deployment using a customer’s private data?Recommended
- Design reliable synchronization between a CRM, data warehouse, and enrichment providers.Recommended
- Design an enterprise architecture for using multiple model providers.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
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?