Staff Machine Learning Engineer - Risk and Trust

Uber; San Francisco (CA); On-site; USD 232,000 - 258,000; Full time

Benefits offered by this job

Bonus program

Equity award

401(k)

Various benefits

Job summary

Uber is seeking a Staff Machine Learning Engineer to own end-to-end ML lifecycle for fraud, identity verification, and account security solutions. You will drive technical leadership, partner with product and data science teams, and deliver measurable business impact from scalable ML models.

Location is San Francisco, onsite, with compensation between USD 232,000 and 258,000 per year. Strong background in Python, PyTorch, TensorFlow, or JAX is required along with Spark/SQL experience.

Qualifications

• 8+ years of 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 with solid foundation in ML algorithms, statistics, and optimization.
• Proven experience applying ML techniques to large-scale, real-world problems.
• Proficiency in Python and frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building large-scale data pipelines and distributed data processing with Spark and SQL-based systems.

Responsibilities

• Collaborate with product, data science, and engineering partners to identify high-impact ML opportunities and shape roadmaps.
• Apply ML techniques to deliver scalable, robust solutions tied to business outcomes.
• Provide technical leadership and mentor engineers to strengthen ML capabilities.
• Own end-to-end ML lifecycle: data prep, feature engineering, model development, deployment, and monitoring.

Skills

ML problem solving

Production ML deployment

End-to-end ML lifecycle

Leadership

Education

Master’s degree (CS/Engineering/Math/Stats)

PhD (preferred)

Tools

Python

PyTorch

TensorFlow

JAX

Spark

SQL

Job description

Uber is building Risk and Trust engineering capabilities that help protect users and improve account safety. This Staff Machine Learning Engineer role supports scalable fraud, identity verification, and account security machine learning solutions, with end-to-end ownership of the ML lifecycle and technical leadership that helps teams deliver measurable business impact.

Location: San Francisco, CA (onsite). Compensation: USD $232,000 - $258,000 per year.

Responsibilities

• Work closely with product, data science, and engineering partners to spot high-impact opportunities, frame complex ML problems, and contribute to the team’s technical roadmap.
• Use deep expertise in machine learning, optimization, and statistics to build innovative, scalable, and robust ML solutions tied to business outcomes.
• Lead technically on complex initiatives, set and drive engineering best practices, and mentor other machine learning engineers to strengthen the team’s capabilities.
• Own the complete ML development lifecycle, including data preparation, feature engineering, model development, system design, implementation, A/B testing, deployment, and production monitoring.

Requirements

• 8+ years of experience developing machine learning models, including classical ML and deep learning, and deploying ML solutions to production.
• Master’s degree in Computer Science, Engineering, Mathematics, Statistics, or a related technical field.
• Strong problem-solving skills with a solid foundation in machine learning algorithms, statistics, and optimization.
• Proven experience applying ML techniques to solve complex, large-scale, real-world problems.
• Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or JAX.
• Experience building large-scale data pipelines and working with distributed data processing frameworks such as Spark and SQL-based systems.

Preferred Qualifications

• PhD in Computer Science, Engineering, Mathematics, Statistics, or a related technical field.
• Hands-on experience with advanced ML techniques such as deep learning, representation learning, sequence modeling, multi-task learning, reinforcement learning, or large language models.
• Experience developing ML solutions for fraud detection, anomaly detection, or other adversarial environments.
• Ability to drive complex ML projects independently from problem formulation through production deployment.
• Experience with large-scale, real-time ML systems and making data-driven decisions in production.

Tech Stack

• Python, PyTorch, TensorFlow, JAX, Spark, SQL

Benefits

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

Work Location Expectations

• 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.