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Source: US DOL Office of Foreign Labor Certification, LCA disclosure data, FY2026 Q2. Government data is published roughly one quarter in arrears.
This advert was matched to Senior Machine Learning Engineer, a job title Airbnb, Inc. has itself sponsored.
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The role, as Airbnb, Inc. describes it
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:
Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community.
You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community.
The Difference You Will Make:
As a Senior Machine Learning Engineer on the Trust team, you will actively contribute code and ideas that shape the ML systems protecting millions of Airbnb users. You'll own and deliver ML projects end-to-end — from designing and training models to productionizing and operating them at scale, while collaborating closely with cross-functional partners.
You'll tackle real-world challenges such as account takeover, fake accounts, payment fraud, and bot detection. Your work will help reduce risks posed by bad actors while ensuring the platform remains seamless and welcoming for everyone else. As you develop your skills, you'll see the tangible impact of your models, helping real users stay safe and confident as they travel, host, and connect on Airbnb.
A Typical Day:
- Collaborate with product managers, data scientists, software engineers, and operations teams to identify opportunities, scope ML solutions, and refine requirements for new or improved Trust models.
- Design, build, and productionize end-to-end Machine Learning pipelines, including feature engineering, model training, evaluation, and deployment — for both batch and real-time use cases.
- Investigate emerging fraud patterns and threat signals with your teammates, and develop ML-based detections and tools that enable faster, more accurate responses.
- Write, review, and ship clean, testable code — whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability.
- Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases.
- Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture.
- Work closely with trust defense and platform teams to adapt models and systems to an evolving landscape of fraud attacks.
Your Expertise:
- 5+ years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale.
- Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent.
- Solid understanding of Machine Learning best practices — e.g., training/serving skew minimization, A/B testing, feature engineering, model selection — and algorithms such as gradient boosted trees, neural networks, transformers, and deep learning.
- Experience with ML frameworks and tool
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