Uber Jobs in California

Staff Machine Learning Engineer

• Uber AI Solutions

Uber AI Solutions is one of Uber's biggest bets with the ambition to build one of the world's largest data foundries for AI applications and evolve into a platform of choice for a variety of online tasks. The Moonshot AI team focuses on optimizing the Uber AI Solutions gig marketplace through intelligent supply and demand matching. We also accelerate human-in-the-loop data annotation with automation and develop robust automated evaluation systems.

We are in the early stages, with significant opportunities to build new ML models for the gig marketplace. This includes integrating advanced ML models to enable ML-assisted annotations for various use cases, such as Gen AI Labeling, Image/Audio/Video Classification, and Image/Video Segmentation, building LLM-as-Judge for automated quality evaluation, training ranking models to recommend gigs to earners, etc.

In this role, you will collaborate closely with product managers, program managers, and cross-functional teams to deliver real world impact. You'll help grow Uber AI Solutions into a leader in the space.

What You'll Do

Contribute to and influence the technical direction for Uber AI Solutions, particularly around ML applications and research.

Identify and choose the right problems that will benefit from ML expertise.

Design and develop a suite of ML models that will help solve the above problems.

Collaborate with backend and frontend engineers to integrate your solutions into our products & platforms.

Explore novel ideas and innovative solutions that can lead to a step change in our products.

Work with cross-functional counterparts like the ML Ops team to understand their needs and improve the models accordingly.

Provide mentorship to engineers on the team and across partner orgs to help raise the technical bar.

Basic Qualifications

Ph.D., MS, or Bachelors degree in Computer Science or a closely related discipline.

Proficiency in Computer Vision (CV), Natural Language Processing (NLP), or Deep Learning, with a solid grasp of the latest advancements in Generative AI.

• Strong desire for continuous learning and professional growth, coupled with a commitment to developing best-in-class systems.
• Excellent problem-solving and analytical abilities.
• Proven ability to collaborate effectively as a team player.

Preferred Qualifications

8

A strong publication record in top-tier AI/ML conferences and journals, demonstrating a history of impactful research.

For San Francisco, CA-based roles: The base salary range for this role is USD$223,000 per year

For Sunnyvale, CA-based roles: The base salary range for this role is USD$223,000 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.

You will also be eligible for various benefits. More details can be found at the following link.

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.