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.

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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

How candidates describe it

Real Senior Machine Learning Engineer interview stories — retold from candidates' public write-ups, with sources.

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