“Typical developer tools hiring pattern. Realistic repository work and shipping judgment often matter more than puzzle-only performance.”
5
stages
2–6 weeks
end to end
18
practice questions
Hiring now
LangChain has 94 open roles right now
landed applies to roles like these for you, on each company’s own site, with answers written for the role.
Application Security Engineer
San Francisco, CA
Business Value Strategist
San Francisco, CA
Revenue Manager
San Francisco, CA
01
The interview, stage by stage
- 1
Recruiter or founder screen30 min
developer experiencecoding agentssystem designPrepare one concrete example and one practice problem for developer experience.
- 2
Live coding or repository task60 min
coding agentssystem designproduct velocityPrepare one concrete example and one practice problem for coding agents.
- 3
Product/system design60 min
system designproduct velocitytechnical communicationPrepare one concrete example and one practice problem for system design.
- 4
Project deep dive60 min
developer experiencecoding agentssystem designPrepare one concrete example and one practice problem for product velocity.
- 5
Team values60 min
coding agentssystem designproduct velocityPrepare one concrete example and one practice problem for technical communication.
02
What decides the offer
Developer experience
25%
Uses specific evidence to demonstrate developer experience.
Coding agents
20%
Uses specific evidence to demonstrate coding agents.
System design
20%
Uses specific evidence to demonstrate system design.
Product velocity
20%
Uses specific evidence to demonstrate product velocity.
Technical communication
15%
Uses specific evidence to demonstrate technical communication.
They look hardest for developer experience, coding agents, system design, product velocity.
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 developer experience
- ·Practice coding agents
- ·Practice system design
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
- How would you defend an LLM application against prompt injection?Recommended
- Design an agent that can safely call external tools.Recommended
- Offline evaluations improved but production metrics fell. What happened?Recommended
- How would you design multi-tenant retrieval without leaking customer data?Recommended
- Design a batching service for model inference.Recommended
- Design an account-research and personalization pipeline for 10,000 prospects.Recommended
- Design an enterprise architecture for using multiple model providers.Recommended
- How do you investigate a production incident involving unsafe model output?Recommended
- How should a team set release thresholds when safety metrics have uncertainty?Recommended
- Design a model router that balances quality, latency, and cost.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 text-to-video generation system.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?