Staff/Principal Machine Learning Engineer, Trust

Role Overview

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. Trust Engineering is responsible for the technology vision and development of a complex stack that runs on every key interaction on the platform. Trust Engineering is at the forefront of AI and ML innovation, helping develop novel techniques and advancing the state-of-art in Machine Learning to continually build and maintain trust across our platform.

The Difference You Will Make:

As a senior technical individual contributor, you will partner closely with our leaders across the broader technical organization helping design, execute and deliver in a complex and collaborative roadmap of Trust engineering efforts that will require collaboration across many parts of the organization and many parts of Airbnb. Although you will be at one of our highest levels of seniority, all individual contributors at Airbnb are Software Engineers which means we expect you to be hands on and contribute code.

A Typical Day

• Define and execute on the long-term ML technical vision and strategy for the Trust organization, identifying key investments, architecting scalable solutions, and championing best practices that advance the state-of-the-art in production ML systems.
• Serve as a technical leader and mentor to other ML and software engineers across the organization, providing guidance on complex architectural and modeling challenges, and raising the overall technical bar.
• Drive and deliver large-scale, multi-quarter ML initiatives that span multiple teams, influencing roadmaps and ensuring alignment between platform and product
• Work with large scale structured and unstructured data, build and continuously improve cutting edge Machine Learning models for Airbnb product, business and operational use cases.
• Work collaboratively with cross-functional partners including software engineers, product managers, operations and data scientists, identify opportunities for business impact, understand, refine, and prioritize requirements for machine learning models, drive engineering decisions, and quantify impact.
• Work closely with other trust defense and platform teams to tackle the changing landscape of fraud attacks.
• Hands-on develop, productionize, and operate Machine Learning models and pipelines at scale, including both batch and real-time use cases.
• Examples include: Anomaly detection models, ML models for continuous risk evaluation, Multimodality and Agentic AI

Your Expertise

• 12+ years of industry experience in applied Machine Learning
• 2-3+ years working with LLMs and novel GenAI technologies. Proficiency and proven experience on Agentic AI (frameworks, orchestration, architecture and productionization).
• A Bachelor’s, Master’s or PhD in CS/ML or related field
• Strong programming (Scala / Python / Java/ C++ or equivalent) and data engineering skills
• Deep understanding of Machine Learning best practices (eg. training/serving skew minimization, A/B test, feature engineering, feature/model selection), algorithms (eg. gradient boosted trees, neural networks/deep learning, optimization) and domains (eg. genAI, Agentic AI, natural language processing, computer vision, personalization and recommendation, anomaly detection)
• Experience with these technologies: AgenticAI, Tensorflow, PyTorch, Kubernetes
• Industry experience building end-to-end Machine Learning and Agentic infrastructure and/or building and productionizing Machine Learning models
• Exposure to architectural patterns of a large, high-scale software applications (e.g., well-designed APIs, high volume data pipelines, efficient algorithms, models)
• Experience with test driven development, familiar with A/B testing, incremental delivery and deployment.
• Experience with the Trust and Risk domain is a plus.