Sr. Machine Learning Engineer

Sr. Machine Learning Engineer Department: Engineering Team: Machine Learning Location: Sao Paulo, Brazil Type: Full-Time _**Please note: this hybrid position is based in São Paulo, Brazil - welcoming both local professionals and those open to relocating to São Paulo.**_ **About the Role** We are dedicated to enhancing the rider experience through cutting-edge machine learning, delivering personalized recommendations and tailored services at scale. Our team develops and deploys state-of-the-art deep learning models that operate in real-time with ultra-low latency, powering experiences that drive high revenue. **About the Team** The Rider ML team is dedicated to developing machine learning infrastructure and ranking solutions that enhance rider engagement across various touchpoints within our platform. This includes optimizing the rider homepage ranking and product selection ranking, among other key engagement screens. Each month, millions of users interact with our platform to request on-demand rides, explore vertical offerings like reservations and rentals, take advantage of relevant promotions, order food and groceries, and subscribe to membership services. Our goal is to recommend the most relevant products based on each user's session intent. Our mission is to build advanced ML infrastructure and algorithms that enable real-time, dynamic personalization of rider surfaces, ensuring a tailored experience for every session. Beyond the homepage and product selection screens, our personalization efforts span multiple engagement touchpoints, including activity feeds, service pages, trip experiences, and other critical moments throughout the rider journey. **What You’ll Work On** - Developing advanced intent modeling and ranking solutions to optimize personalized recommendations. - Striking the right balance between ranking relevance and discovery (exploration vs. exploitation). - Researching and integrating new signals to improve key ranking metrics and user engagement. - Building and deploying ML models at scale, ensuring high reliability and quality in online serving. **Basic Q