The Marketplace Signals team at Uber is responsible for building and optimizing foundational marketplace signals that power user experiences and drive marketplace efficiency. Our team ensures that key signals—such as eyeball ETA, spinner time, and supply reliability indicators—are leveraged effectively across various Uber products and levers, enabling data-driven decision-making and seamless coordination across different business functions.
What You'll Do
- Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
- Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
- Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
- Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
What You'll Need
- 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
Why Join Us?
- Work on high-impact machine learning problems that directly improve Uber's marketplace efficiency.
- Influence key business levers that optimize Uber’s pricing, matching, and rider/driver experience.
- Build centralized marketplace signals that reduce redundancy and improve operational efficiency.
- Join a high-caliber, innovative team tackling some of the hardest ML challenges in the industry.
If you’re passionate about using ML to optimize real-world systems at a massive scale, we’d love to hear from you!
What You Will Do:
- Develop and optimize ML models to enhance key marketplace signals (e.g., ETA predictions, supply availability metrics, demand forecasts).
- Collaborate with cross-functional teams (Pricing, Matching, Driver Incentives, etc.) to ensure marketplace signals are effectively utilized.
- Improve operational efficiency by building a centralized, scalable system for marketplace signals that serves multiple use cases.
- Ensure consistency and reliability across Uber's platform by maintaining high-quality marketplace signals that inform rider and driver experiences.
- Reduce technical debt by streamlining signal infrastructure and minimizing redundant computations.
- Leverage cutting-edge ML techniques (deep learning, probabilistic modeling, reinforcement learning, etc.) to continuously refine marketplace signals.
- Work with real-time streaming data and large-scale distributed systems to ensure Uber's signals are up-to-date and responsive to market dynamics.
Basic Qualifications:
- Ph.D. or M.S. in Statistics, Economics, Mathematics, Computer Science, Machine Learning, Operations Research, or other quantitative fields.
- 6+ years of industry experience in machine learning, including building and deploying ML models at scale.
- Experience in modern deep learning architectures and probabilistic modeling
- Proficiency in programming languages (Python, Java, Scala) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn),
- Solid understanding of MLOps practices, including design documentation, testing, and source code management with Git.
- Advanced skills in the development and deployment of large-scale ML models and optimization algorithms
- Strong business and product sense: ability to shape vague questions into well-defined analyses and success metrics that drive business decisions.
Preferred Qualifications:
- Expertise in developing causal inference methodologies, experimental designs, and advanced analytical methods.
- Strong experience in building a wide range of models (e.g. causal inference, optimization, ML) for business applications.
- Experience in algorithm development and rapid prototyping.
- Design, develop, and operationalize econometric models to assess challenging causal problems such as product incrementality and long-term value
- Propose, design, and analyze large scale online experiments and interpret the results to draw actionable conclusions.
- Ability to drive clarity on the best modeling solution for a business objective.
- Collaborate with cross-functional teams across disciplines such as product, engineering, and operations to drive system development end-to-end from generating ideas to productionizing.
For San Francisco, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.
For Sunnyvale, CA-based roles: The base salary range for this role is USD$232,000 per year - USD$258,000 per year.
For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits. More details can be found at the following link https://jobs.uber.com/en/benefits.
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 fuels progress. What moves us, moves the world - let's move it forward, together.
Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
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.