**About the Role**

The Rider team is the centerpiece of the Uber consumer experience, owning the core flagship app used by over 100M+ monthly active users.

The Personalization team is looking for a Senior Scientist who can help us innovate on our AIML efforts  and tackle the next frontier of challenges, including ranking & recommender systems, and agentic applications.

As a Scientist, you will play a critical role in enhancing the personalization AIML experience for millions of Rider app users worldwide. You will leverage your expertise in machine learning and data science to optimize our ranking & recommendation systems, help develop agentic applications, and simulate marketplace outcomes, ultimately improving user satisfaction and achieving business growth through one of the most impactful channels.

**What You'll Do**

- Prototype new models and methodologies (e.g. evidential deep learning, reinforcement learning, listwise ranking) for improving performance across the various personalization AI/ML surfaces of the rider app.
- Deep dive the data and optimize current ranking algorithms to enhance the relevance and accuracy of ranking results.
- Design experiments and offline simulations to measure the performance of AIML systems, including agentic applications, interpret the results, and make impactful recommendations for areas of development
- Work closely with product managers, engineers, and other scientists to define project goals and deliver data-driven solutions.
- Stay current with the latest advancements in personalization AIML
- Mentor and review the work of junior team members

**Basic Qualifications**

- M.S. or Bachelor's degree in Computer Science, Statistics, Economics, Mathematics, Operations Research, or other quantitative fields.
- 5+ years of industry experience as an Applied Scientist, Research Scientist, or equivalent.
- Proficiency in programming languages (Python, PySpark, SQL) and ML frameworks (TensorFlow, PyTorch, Scikit-Learn),
- Strong business and product sense: ability to find business opportunities in AIML Systems and help define product and engineering roadmaps to capture these opportunities.

**Preferred Qualifications**

- Prior experience with feed ranking, recommender systems, or search algorithms
- 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

For San Francisco, CA-based roles: The base salary range for this role is USD$183,000 per year - USD$203,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link [https://www.uber.com/careers/benefits](https://www.uber.com/careers/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](https://forms.gle/aDWTk9k6xtMU25Y5A).

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.