About the role and team
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 challenge and motivated by real-world impact, this is where you’ll grow!
As an Applied 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 do
- Design and implement ML models 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 models end-to-end, from the first scientific hypothesis to debugging production issues in real-time, low-latency environments.
Time spent in the day
- 40% Algorithm development, model training, and deep learning research.
- 30% Designing experimentation frameworks and performing causal inference analysis.
- 20% Cross-functional collaboration with MLEs and Product Managers to align on roadmaps.
- 10% Monitoring production performance and improving system hygiene/technical debt.
Basic Qualifications
- 5+ years of experience (or Ph.D. equivalent) in an Applied Science, Machine Learning, or Data Science role.
- Specialized domain expertise in Ranking, Recommender Systems (RecSys), or Search.
- Proven experience in training and deploying Deep Learning models at scale within a production environment.
- Proficiency in Python and SQL with experience handling large-scale datasets using Spark, Hive, or PySpark.
- Solid understanding of statistical methods, experimental design, and A/B testing.
- BSc., M.S., or Ph.D. in Computer Science, Machine Learning, Statistics, Economics, or a related quantitative field.
Preferred Qualifications
- 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.
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.