Airbnb

Sep 30, 2026

Senior Staff Machine Learning Engineer, Data & Eval

United States

About the opportunity

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.

Develop and productionize pipelines for dataset creation, model monitoring, evaluation-at-scale, and continuous testing (pre-deploy and post-deploy).

Drive technical decisions and architecture for evaluation and data infrastructure, balancing speed, rigor, cost, and safety.

Minimum Qualifications

Educational Background: PhD in Computer Science, Mathematics, Statistics, or related technical field (or equivalent practical experience).

Industry Experience: 10+ years building, testing, and shipping ML/AI systems end-to-end; including 2+ years of experience with GenAI/LLM systems in production.

Leadership Experience: 5+ years leading large, ambiguous technical initiatives as a senior IC, influencing roadmap and engineering/science direction across teams.

Technical Proficiency:

Deep expertise in evaluation methodology (offline/online alignment, metric design, human-in-the-loop evaluation, A/B testing, power analysis, regression testing).

Hands-on experience with GenAI systems, including orchestration, retrieval, tool calling, memory, etc. *

Experience building data pipelines and quality systems (labeling workflows, dataset curation, versioning, monitoring, and governance).

Solid ML fundamentals and best practices (model selection, training/serving, monitoring, reliability, and model lifecycle management).

Preferred Qualifications

Customer Support Systems: Experience applying ML/AI to customer support workflows (e.g., agent assist, classification/routing, resolution recommendation, QA).

Infrastructure & Quality at Scale: Experience building robust evaluation platforms for agent behavior validation, safety/guardrails, and continuous improvement.

Agile Practice for Applied AI: Proven ability to take evaluation and data flywheel work from incubation to production, iterating quickly while maintaining scientific rigor.

Your Location

This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager.

While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a registered entity. Click here careers.airbnb.com/ for the up-to-date list of excluded states. This list is continuously evolving, so please check back with us if the state you live in is on the exclusion list. If your position is employed by another Airbnb entity, your recruiter will inform you what states you are eligible to work from.

Equal Employment Opportunity

Airbnb values diversity & belonging and is proud to be an Equal Employment Opportunity employer. All individuals seeking employment at Airbnb are considered without regard to race, color, religion, national origin, age, gender, sex, marital status, ancestry, physical or mental disability, veteran status, gender identity, gender expression, sexual orientation, or any other legally protected characteristic.

Reasonable Accommodations

We are committed to providing reasonable accommodations throughout the recruitment process for applicants with disabilities. If you need assistance, or an accommodation, please let your recruiter know once you are contacted about a role.

A Note on Recruiting Scams

Scammers sometimes pose as Airbnb recruiters to get money or personal information from candidates. A few things that will always be true for Airbnb’s hiring process: our open roles are posted on Airbnb’s Career’s Page at careers.airbnb.com, and our recruiters correspond only from @airbnb.com airbnb.com or @ext.airbnb.com ext.airbnb.com email addresses.

Our recruiters will never ask for your Social Security number, bank account details, passport, or payment app information while you are interviewing. We’ll also never ask you to pay a fee, send money, deposit or cash a check, or purchase work-related equipment (such as a company laptop) during the interview process. We encourage candidates to remain vigilant of these recruiting scams and not share sensitive information if you do not believe an individual is actually affiliated with Airbnb.

How We'll Take Care of You

Our job titles may span more than one career level. The actual base pay is dependent upon many factors, such as: training, transferable skills, work experience, business needs and market demands. The base pay range is subject to change and may be modified in the future. This role may also be eligible for bonus, equity, benefits, and Employee Travel Credits.

Pay Range

$244,000—$305,000 USD

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