About the Role
Uber’s newly formed AI Security team, part of the Core Security Engineering organization, is building the foundation for dynamic, data-driven security systems. We’re evolving Uber’s Zero Trust Architecture (ZTA) to be more risk-adaptive across authentication and authorization, moving beyond static rules and manual approvals toward real-time, ML-driven access decisions that secure both humans and AI agents without slowing them down.
As a Senior ML Engineer, you’ll translate ambiguous business and security needs into concrete ML problems, design and iterate on solutions, and take them end-to-end into production. This is greenfield work at the intersection of ML, security, and infrastructure, shaping how Uber secures AI at scale.
What the Candidate Will Need / Bonus Points
---- What the Candidate Will Do ----
- Translate business and security needs into well-defined ML problems.
- Develop, iterate, and productionize ML models that drive risk-adaptive decisions in real-time.
- Engineer features from Uber’s risk systems, logs, and contextual signals.
- Integrate ML systems into Uber’s critical access pathways (containers, APIs, gateways, data).
- Collaborate across Security, Risk, and Infra teams to deliver scalable, production-ready solutions.
- Provide leadership by mentoring junior engineers, evangelize ML best practices, and help shape ML strategy within AI Security.
---- Basic Qualifications ----
- 5+ years experience in formulating ML problems from ambiguous business requirements, especially in risk, fraud, or security contexts.
- Proficiency across a broad range of ML algorithms: tree-based models (XGBoost, LightGBM), classical statistical models (logistic regression, SVMs), and deep learning architectures (CNNs, RNNs, Transformers), with the ability to select and apply the right approach based on context and data characteristics.
- Hands-on experience with feature engineering, model development, and productionization of ML pipelines.
- Proficiency in PyTorch, TensorFlow, or similar ML frameworks, and in Python or comparable languages for scalable, production-grade systems.
---- Preferred Qualifications ----
- Proven ability to own ML systems end-to-end: from requirement discovery → feature design → modeling → deployment.
- Deep experience with advanced ML techniques, including ensemble methods, neural networks, graph-based models, and handling challenges like imbalanced data, feedback loops, and iterative retraining.
- Familiarity with large-scale data/infra systems (Kafka, Pinot, Hive, Cassandra, Spark, Flink).
- Background in access control, authentication, or enterprise security systems.
- Track record of technical leadership: mentoring engineers, driving cross-functional initiatives, or shaping ML/security strategy.
For San Francisco, CA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year.
For Seattle, WA-based roles: The base salary range for this role is USD$202,000 per year - USD$224,000 per year.
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. More details can be found at the following link https://jobs.uber.com/en/benefits.
Uber's mission is to reimagine the way the world moves for the better. Here, bold ideas create real-world impact, challenges drive growth, and speed fuels progress. What moves us, moves the world - let's move it forward, together.
Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.
Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.