Staff Machine Learning Engineer

Uber Freight Marketplace is modernizing the UF marketplace team through end-to-end machine learning ownership. As a Staff Machine Learning Engineer based in Chicago, IL (onsite), you will help develop and productionize ML models that support key marketplace decisions, partnering closely with backend engineers to ensure model performance in real-world systems.

In this role, you will focus on building ML capabilities for the UF marketplace, including model development for cost prediction, booking probability, and demand elasticity. You will maintain responsibility across the lifecycle of these models, from development through integration with production backends.

What you’ll do

• Provide end-to-end ownership for ML models powering the UF marketplace, including cost prediction, booking probability, and demand elasticity.
• Collaborate with backend engineers to productionize ML components and ensure they work as expected after deployment.

What you bring

• 6+ years of experience developing ML models to address business problems.
• Bachelor’s degree in Computer Science, Computer Engineering, or a related field.
• Familiarity with modern AI/ML frameworks, such as PyTorch.

Technology

• PyTorch

Bonus, equity, and benefits

• Eligible to participate in Uber’s bonus program.
• May be offered an equity award and other forms of compensation.
• Eligible to participate in a 401(k) plan.
• Eligible for various benefits.

Preferred qualifications

• Product experience is a major plus.
• Adaptive development of ML models to fit business context is critical.
• Prior experience with state-of-the-art marketplace technology is preferred.
• Experience with causal inference and constrained optimization is preferred.

Office presence is important: Unless approved for full remote work, employees must spend at least 50% of their time in-office. Some roles, including those at greenlight hubs, require full-time in-office presence.

Compensation: The estimated salary range for this role is USD 209,000 - 232,000 per year.