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Uber Staff Machine Learning Engineer 
United States, West Virginia 
962828317

Yesterday
Please note: this hybrid position is based in São Paulo, Brazil - welcoming both local professionals and those open to relocating to São Paulo.

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.

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
  • 5+ 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)
  • Industry 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

  • 7+ 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.
  • Innate truth-seeker who values and produces analytic evidence and insight, as well as translating them and business goals into technical problems and solutions.
  • 3+ years of 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
  • Passionate about helping junior members grow by inspiring and mentoring engineers
  • Resilience, determination, ownership mindset

* 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 .