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 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 (Project Lumos) and product selection ranking (Project Aura), 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.

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.

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 Qualifications

• Bachelor's degree in Computer Science, Engineering, Mathematics or related field

• 3 years of experience in software engineering with an emphasis on data-driven methodologies, deep learning, and online experimentation

• Strong problem-solving skills, with expertise in ML methodologies

• Experience in applying ML, statistics, or optimization techniques to solve large-scale real-world problems (e.g. ads tech, recommender systems)

• Experience in ML frameworks (e.g. Tensorflow, Pytorch, or JAX) and complex data pipelines; programming languages such as Python, Spark SQL, Presto, Go, Java

Preferred Qualifications:

• 3+ years of experience in software engineering specializing in applied ML methods
• Experience in designing and crafting scalable, reliable, maintainable and reusable ML solutions using deep-learning techniques and statistical methods.
• Detail-oriented, ownership and truth-seeking mindset.
• Values and produces analytic evidence and insight, as well as applying them to improve technical solutions.
• Experience working in a cross-functional and/or cross-business projects, partnering with Product, Scientists, and cross-org leads to shape the team's strategies
• Master's degree in Computer Science, Engineering, Mathematics or related field

Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuelds progress. What moves us, moves the world - let's move it forward, together.

Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.

Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to accommodations@uber.com.