Senior Machine Learning Engineer (Trust and Safety)
As a Senior Machine Learning Engineer on the Trust & Safety team at Yelp, you will be responsible for building and deploying end-to-end ML systems to detect and understand content spam, ensuring high-quality and trustworthy user experiences. You will leverage cutting-edge ML/AI tools like neural networks and large language models, working with a vast dataset to connect users with great local businesses.
Yelp's engineering culture thrives on cooperation, authenticity, and creative problem-solving. New engineers deploy code within their first week, and individual impact is broadened through manager, mentor, and team support. The ultimate goal is to help users, foster engineer growth, and maintain a collaborative environment.
Yelp manages hundreds of millions of user-contributed content pieces, millions of users and business listings, and hundreds of thousands of advertising customers, all growing constantly. Extracting insights from this vast and complex data, understanding relationships between variables, and deciphering interactions are challenging but critical for Yelp's business success.
The Trust & Safety team at Yelp is dedicated to presenting high-quality content that genuinely reflects the experiences of real Yelpers engaged with the community. We heavily invest in ML models and infrastructure to detect and comprehend content spam on our platform. As an engineer on this team, you will contribute daily to helping users make trustworthy connections with excellent local businesses, assisting them in decisions like dining choices, mover selection, or finding the best places to visit in a new city. Yelp's mission to connect people with great local businesses necessitates the use of cutting-edge Machine Learning (ML) and Artificial Intelligence (AI) to scale across a vast and diverse user and business base spanning various geographical locations. As an ML engineer, you will have the opportunity to foster these connections across millions of users and business listings utilizing industry-leading tools such as neural networks (NNs), large language models (LLMs), and traditional ML methods like XGBoost or linear models.
Posted June 10, 2026