Staff Machine Learning Engineer - Risk and Trust

Job description

About the Role We are looking for an experienced Staff Machine Learning Engineer to join Uber's Risk and Trust engineering organization. Our team plays a critical role in protecting users and maintaining the integrity of Uber's global platform through advanced fraud prevention, identity verification, and account security solutions. As fraud threats continue to evolve, including increasingly sophisticated AI-driven attacks, we leverage cutting-edge machine learning techniques to build scalable, intelligent risk detection and prevention systems.

In this role, you will lead the development of high-impact ML solutions, tackle complex technical challenges, and collaborate closely with cross-functional partners to deliver measurable business impact while maintaining seamless user experiences.

What You'll Do

Collaborate with product, data science, and engineering partners to proactively identify high-impact business opportunities, formulate complex ML problems, and help shape the team's technical roadmap. Apply deep expertise in machine learning, optimization, and statistics to design and develop innovative, scalable, and robust ML solutions that drive measurable business impact. Provide technical leadership on complex ML initiatives, drive engineering best practices, and mentor other MLEs to elevate the team's technical capabilities.

Own the end-to-end ML development lifecycle, including data preparation, feature engineering, model development, system design, implementation, A/B testing, deployment, and production monitoring. Partner with cross-functional teams to productionize ML solutions at scale.

Basic Qualifications

8+ years of industry 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 and a solid foundation in machine learning algorithms, statistics, and optimization. Demonstrated 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. Demonstrated ability to independently drive complex ML projects from problem formulation through production deployment.

Experience working with large-scale, real-time ML systems and making data-driven decisions in production environments.

Responsibilities

For San Francisco, CA-based roles

The base salary range for this role is USD $232,000 per year - USD $258,000 per year.

For Sunnyvale, CA-based roles

For all US locations, 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.