Staff ML Engineer — Fraud & Risk Trust Systems

Uber Technologies Inc.; Sunnyvale (CA); On-site; USD 232,000 - 258,000; Full time

Job summary

Uber Technologies Inc. is seeking a Staff Machine Learning Engineer to join the Risk and Trust engineering organization.

You will lead the development of high-impact ML solutions to detect fraud, verify identities, and protect accounts across Uber's global platform. You will own the end-to-end ML lifecycle, collaborate with cross-functional teams, mentor other engineers, and help scale production ML systems while maintaining seamless user experiences.

Qualifications

• 8+ years of industry experience developing ML models and deploying to production.
• Master's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
• Strong problem-solving skills and foundation in ML, statistics, and optimization.
• Experience applying ML techniques to large-scale real-world problems.
• Proficiency in Python and ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building large-scale data pipelines and working with Spark and SQL-based systems.

Responsibilities

• Collaborate with product, data science, and engineering to identify high-impact ML opportunities and shape the roadmap.
• Apply ML, optimization, and statistics to design scalable, robust solutions with business impact.
• Provide technical leadership and mentor other MLEs to elevate capabilities.
• Own the end-to-end ML lifecycle from data prep to deployment and monitoring.

Skills

ML Experience

Python

Problem Solving

Communication

Education

Master's degree in CS/Engineering/Math/Stats

PhD (preferred)

Tools

PyTorch

TensorFlow

JAX

Spark

SQL

Job description

Uber Technologies Inc. is seeking a Staff Machine Learning Engineer to join the Risk and Trust engineering organization.

You will lead the development of high-impact ML solutions to detect fraud, verify identities, and protect accounts across Uber's global platform. You will own the end-to-end ML lifecycle, collaborate with cross-functional teams, mentor other engineers, and help scale production ML systems while maintaining seamless user experiences.