Staff Machine Learning Engineer
About the role
Uber Freight Marketplace is building the next generation of logistics technology by leveraging Uber's proven marketplace playbook to freight. As part of a small, high-impact team, you'll help bring expertise from Uber's mobility and delivery marketplaces into a rapidly evolving industry, developing pricing, matching, recommendation, and optimization systems that will disrupt the freight industry ($1T TAM). Uber Freight is at a very early stage, with just 0.4% of the pie now, and it's a rare opportunity to solve challenging marketplace problems with AI/ML and marketplace optimization while shaping how the freight industry transforms.
Responsibilities
- End-to-end ownership of ML models in the Uber Freight marketplace (cost prediction, booking probability, demand elasticity, etc.).
- Develop ML models and collaborate with backend engineers to deploy them into production, ensuring they perform as expected.
Requirements
- 6+ years of experience developing ML models to solve business problems.
- Bachelor's degree in Computer Science, Computer Engineering, or related fields.
- Familiarity with modern AI/ML frameworks (e.g., PyTorch).
Preferred Qualifications
- Product experience, particularly in adaptive development of ML models to business contexts.
- Previous experience with state-of-the-art marketplace technology.
- Experience with causal inference and constrained optimization.
Pay
- Chicago, IL: USD $209,000 – $232,000 per year.
- New York City, NY: USD $232,000 – $258,000 per year.
- San Francisco, CA: USD $232,000 – $258,000 per year.
- Seattle, WA: USD $232,000 – $258,000 per year.
- Sunnyvale, CA: USD $232,000 – $258,000 per year.
For all US locations, you will be eligible to participate in Uber's bonus program and may be offered equity awards and other compensation. All full-time employees are eligible for a 401(k) plan and various benefits.
Schedule
Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, like those at greenlight hubs, require full-time in-office presence.