Senior Staff Machine Learning Engineer, Data & Eval

Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way.

The Community You Will Join:

AI and ML are at the heart of the Airbnb product. From Trust to Payments, and from Customer Service to Marketing, we rely on ML to ensure that guests and hosts have the best possible experience with Airbnb.

The Core ML team is responsible for driving Airbnb Assistance Engineering initiatives by adopting Generative AI technologies to enable an intelligent, scalable, and exceptional service experience. The team develops and enhances AI models, ML services, and tools including LLM fine-tuning and optimization, RAG/Search, LLM evaluation and testing automation, feedback-based learning, and guardrails for a wide range of applications at Airbnb.

The richness of Airbnb's data, the complexity of its marketplace, and the variety innate in our product mean that we need to operate at the state of the art of AI practice. We are committed to long-term innovation to solve complex problems, and to do that we need experienced ML

​​The Difference You Will Make:

In this Senior Staff Machine Learning Engineering role, you will set technical direction and lead execution for ML evaluation and the end-to-end data flywheel powering Airbnb Assistance Engineering (e.g., assistive agents, issue resolution, and tooling). Your work will define how we measure quality, how we turn feedback into learning signals, and how we continuously improve models and products safely and efficiently. You will partner closely with product, engineering, design, operations to build evaluation systems that are trusted, scalable, and actionable - connecting offline metrics to online outcomes.

A Typical Day:

• Define evaluation strategy and success metrics for GenAI systems, aligning offline evaluation with online business and customer experience outcomes.

• Build and scale evaluation frameworks (golden sets, synthetic data, automated regressions, rubric-based grading, LLM-as-judge where appropriate) with strong controls for bias, drift, and reliability.

• Design the data flywheel: instrumentation, feedback collection, data quality checks, labeling strategy, dataset versioning, and governance to support continuous improvement.

• Lead cross-functional quality initiatives across product, ops, and engineering, driving clarity on what “good” looks like and how teams act on evaluation results.