Sr Staff Machine Learning Engineer - Uber AI Solutions

About the role and team 

Uber AI Solutions (UAIS) is a startup inside Uber, building the data and evaluation infrastructure behind the next generation of AI. The models making headlines are only as good as the data and feedback they learn from, and that is the work we do.

We are an agile team moving at startup speed with the full weight of Uber behind us: global reach, world class infrastructure, and one of the largest human networks in the world. We pair that network with machine learning to produce the high quality, model ready data that the most advanced AI teams on earth depend on. Our work spans every modality, from text, images, and video to audio and physical AI and AVs, and reaches across dozens of countries.

This is a place for builders who want real ownership. Here you own a problem, not a ticket: you take a vertical from start to finish, the machine learning, the backend systems, and the product, and drive it from a blank page to something the best customers in AI rely on every day. You work shoulder to shoulder with product managers and other exceptional engineers, go as deep as the problem demands, and make the calls that decide whether your area wins. It is the closest thing to running your own company, with a rocket strapped to it.

If you want your work to show up in the AI systems shaping the world, and you want to build it like a founder, this is the team.

What you’ll do 

• Build the ML and backend systems that make the growth real and durable, going deep on the hardest problems yourself.

• Own your vertical end to end: set the direction, find where the growth is, and drive the business metrics that matter, such as revenue, customers, and market reach.

• Move fast and decisively in ambiguity, making the strategic and technical calls that decide whether the business wins.

• Be a trusted technical partner in the most important customer relationships, using them to grow the account and shape what you build next.

• Build the team and raise the bar so the business can keep scaling.

  
  

• Master's or PhD in Computer Science, Machine Learning, Electrical Engineering, Mathematics, or a related discipline.

• 7+ years building and shipping products powered by ML, with a history of owning both the technology and the business results for a significant product area.

• Proven track record of growing a business, not just running it: you have driven significant, measurable business growth such as revenue, adoption, or market expansion.

• Deep expertise in both machine learning systems and backend distributed systems, with the ability to build production systems that scale as the business grows.

• A track record of finding the highest leverage opportunities in an ambiguous space and driving them to real business impact.

• Exceptional communication and leadership skills, with the ability to align customers, product, and business leadership around a growth agenda.

• You have taken a product or business line from early stage to meaningful scale, ideally in a startup or startup like environment, owning revenue and growth throughout.

• Experience as the owner of an applied AI product domain, such as real world data, agentic environments, evaluation, or multimodal and multilingual ML, where you drove significant business outcomes.

• Expertise in GenAI and LLM systems at production scale, spanning architecture, evaluation, safety, and responsible deployment.

• Depth in a domain central to UAIS such as physical world and physical AI data, agentic RL environments and evaluation, multilingual and audio, or robotics and egocentric data, and a track record of owning strategic customer relationships.
   

For San Francisco, CA-based roles: The base salary range for this role is USD $267,000 per year - USD $297,000 per year.

You will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. All full-time employees are eligible to participate in a 401(k) plan. You will also be eligible for various benefits.

Ready to Ride?