Staff Machine Learning Engineer — Foundation Models Leader

Uber; San Francisco (CA); On-site; USD 232,000 - 258,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k) plan

Benefits

Job summary

Uber is seeking a Staff ML Engineer (IC6) to define and lead the foundation model strategy powering discovery experiences across Mobility and Delivery. You will shape technical direction, influence product strategy, and deliver measurable impact at global scale.

You will own end-to-end model strategy, drive important architecture decisions, and mentor senior engineers, contributing to a high-impact AI org within Uber.

Qualifications

• Masters degree or Ph.D in Computer Science, Engineering, Mathematics.
• 8+ years of ML experience, including large-scale deep learning systems.
• Ownership of high-impact ML systems in search, recommendations, or conversational AI.

Responsibilities

• Own 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-tine vs partner models).
• Mentor senior engineers and act as a technical multiplier across the org.

Skills

ML experience

Transformers expertise

PyTorch

Distributed training

Ownership of high-impact ML systems

Product intuition

Education

Masters or PhD in CS/Engineering/Math

Tools

PyTorch

Distributed training frameworks

Job description

Uber is seeking a Staff ML Engineer (IC6) to define and lead the foundation model strategy powering discovery experiences across Mobility and Delivery. You will shape technical direction, influence product strategy, and deliver measurable impact at global scale.

You will own end-to-end model strategy, drive important architecture decisions, and mentor senior engineers, contributing to a high-impact AI org within Uber.