Airbnb
Senior
Build the agentic AI stack behind Airbnb's Chat, Voice, and Human-Agent Assist products
Airbnb's Community Support ML team owns the Chat AI assistant, Voice AI Assistant, and Human Agent Assistant that serve guests and hosts worldwide. This interview probes hands-on LLM expertise — fine-tuning, RAG, and multi-agent orchestration — plus the ability to ship production-grade ML systems and work cross-functionally with product and design from ambiguous, early-stage ideas to shipped features.
Step into this interview
Free · a live voice mock calibrated to this exact role
What this interview tests
- LLM fine-tuning (SFT, RLHF, GRPO) and prompt engineering
- Agentic system design (multi-agent orchestration, tool-use, memory, ReAct/LangGraph-style pipelines)
- RAG architecture for grounding responses in account/policy context
- Production ML infrastructure, model serving, and MLOps at scale
- LLM evaluation frameworks and guardrails for a customer-facing assistant
- Cross-functional communication with product, design, and engineering on 0-to-1 initiatives
Common question themes
Design a chat or voice AI agent that can safely escalate to a human when uncertain
Describe a time you fine-tuned or distilled an LLM and how you measured the improvement
How would you build an evaluation framework for a customer-support AI assistant
Tradeoffs between building a custom agentic pipeline vs. using an existing framework
How do you guard against hallucinated policy answers in a support context