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# Staff Software Engineer

- **Company:** Uber
- **Location:** Seattle, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Distributed Systems, Machine Learning, Recommender Systems, Systems Architecture, Information Technology, Machine Learning Operations
- **Published:** June 27, 2026
- **Apply:** https://dejobs.org/x/x/15A08B0B484D41EAAD89FF16B7E86B61/job/

## About the Role

* Ph.D., M.S. or Bachelor in Computer Science, Mathematics with focus on Machine Learning, or equivalent technical background with exceptional demonstrated impact
 * 8+ years experience leading the development and deployment of ML models in large-scale production environments at top-tier ML companies, * Expertise in search, recommendation systems, ranking/retrieval, or representation learning is highly desirable.
 * Proven experience in ranking optimization across heterogeneous content types.
 * Demonstrated success in leading cross-functional projects that deliver significant business impact.

## Description

Staff Machine Learning Engineers at Uber are passionate and pragmatic technologists who are able to translate business insight and goals into well-formulated ML projects and scalable solutions to deliver impact. They are not only collaborative role models but also approachable leaders, humble teachers while also effective in helping the team in project execution. You will work with talented people in product, science, operations, and platform teams to help build and optimize our Rider Experience products. The role requires technical chops as well as strong communication & leadership skills.

What You'll Do

 * Define and execute technical strategies, spanning from model and system architecture to business objectives and stakeholder alignment.
 * Lead the design, development, and production of end-to-end ML solutions for large-scale distributed systems serving billions of trips.
 * Lead and mentor a team of Machine Learning Engineers (MLEs), providing technical leadership, setting the vision, and guiding the team through the full development lifecycle-from ideation to model deployment and scaling.


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