Role overview
Working at Uber means solving hard problems in a high-stakes, fast-moving environment. You'll need to take ownership, stay adaptable, and build with both urgency and care. If you're energized by a challenge and motivated by real-world impact, this is where you'll grow!
As a Scientist on the Discovery Science team, you will move the needle for the business through strong product execution at the intersection of ML research and marketplace algorithms. This isn't about tuning models in a vacuum; it's about navigating the messiness of a multi-sided ecosystem where performance, safety, and scale are inseparable. You will partner with engineers to architect the next generation of RecSys, balancing technical rigor with the pressure of real-world traffic and shifting business priorities.
What you'll work on
- Design and implement ML algorithms and objective functions that unify competing business interests like organic relevance and sponsored content into a single value space.
- Act as the science lead for foundational machine learning initiatives, unblocking technical debt and optimizing feature engineering for high-scale, real-time systems.
- Navigate the ambiguity of user behavior by designing sophisticated experiments and causal inference frameworks that go beyond standard A/B testing.
- Collaborate across disciplines (Product, Engineering, and Data Science) to translate high-level business goals into theoretically sound and performant technical roadmaps.
- Research and apply advancements in Deep Learning, Reinforcement Learning, and GenAI to solve complex, high-impact problems without a clear starting point.
- Own your algorithms/ML workflow, from the first scientific hypothesis to debugging production issues in real-time, low-latency environments.
What we're looking for
- Domain expertise in Ranking, Recommender Systems (RecSys), or Search.
- Experience with advanced modeling techniques like Reinforcement Learning, multi-task learning, or auto-regressive models.
- Ability to communicate complex scientific results to both technical and non-technical stakeholders to influence business strategy.
- Familiarity with deploying production-grade pipelines into real-time, low-latency systems using Kafka or Pinot. Strong systems thinking and the ability to make smart trade-offs between short-term velocity and long-term scientific rigor.
For San Francisco, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$190,000 per year - USD$211,000 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.