About the Team:

The Applied AI team collaborates with product teams across Uber to deliver innovative AI solutions for core business problems. We work closely with engineering, product and data science teams to understand core business problems and the potential for AI solutions, then deliver those AI solutions end-to-end. Key areas of expertise include Personalization, Generative AI, Computer Vision, ML Optimization and Geospatial AI.

About the Role:

We are building AI-native discovery experiences across Mobility and Delivery. Search, recommendations, and conversational AI are central to how millions of users discover rides, restaurants, grocery items, and retail products every day. We are hiring a Staff ML Engineer (IC6) to define and lead the foundation model strategy powering these experiences.

At this level, you will not just build models — you will shape technical direction across teams, influence product strategy, and deliver measurable impact at global scale.

What the Candidate Will Do

• Own the end-to-end technical strategy for foundation models across Search, Recommendations, and Conversational AI.
• Drive architecture decisions that influence multiple product surfaces (Eats, Grocery, Retail, Mobility).
• Lead cross-team initiatives spanning Retrieval, Ranking, Personalization, and LLM-powered assistants.
• Define long-term investment areas (build vs fine-tune vs partner models).
• Mentor senior engineers and act as a technical multiplier across the org.

Basic Qualifications

• Masters degree or Ph.D in Computer Science, Engineering, Mathematics
• 8+ years of ML experience, including significant work on large-scale deep learning systems.
• Demonstrated ownership of high-impact ML systems in search, recommendations, or conversational AI.
• Deep expertise in transformers, retrieval systems, ranking, and embedding architectures.
• Strong experience with PyTorch and distributed training .
• Track record of influencing technical direction across teams.
• Strong product intuition and ability to connect model improvements to business outcomes.

Preferred Qualifications

• Experience leading multi-team ML initiatives.
• Defined long-term technical roadmaps adopted across orgs.
• Elevated engineering standards through mentorship and technical leadership.

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 Seattle, WA-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.