Machine Learning Engineer

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.

As an ML Engineer, you’ll help translate business and security needs into concrete ML problems, build models and features, and take them into production. You’ll be part of a team working on greenfield projects 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 ----

1. Support framing business and security problems as ML tasks.
2. Build and iterate ML models that enable risk-adaptive, real-time decisions.
3. Engineer features from Uber’s risk systems, logs, and contextual signals.
4. Deploy and maintain ML pipelines in production, ensuring reliability and scalability.
5. Collaborate with senior engineers to integrate ML into Uber’s authentication and authorization systems.

---- Basic Qualifications ----

1. 3+ years experience building and deploying ML models in production, with hands-on work in feature engineering, training, and evaluation.
2. Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar).
3. Strong foundation in ML algorithms: tree-based models (XGBoost, LightGBM), classical methods (logistic regression, SVMs), and exposure to neural networks (CNNs, RNNs, Transformers).
4. Ability to analyze business/security requirements and support translating them into ML use cases.

---- Preferred Qualifications ----

1. Experience with risk, fraud, anomaly detection, or security-related ML systems.
2. Familiarity with large-scale data/infra systems (Kafka, Hive, Spark, Flink, Pinot).
3. Exposure to handling challenges such as imbalanced data, feedback loops, or iterative retraining.
4. Strong communication skills and ability to work cross-functionally with infra, risk, and security teams.

For San Francisco, CA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 per year.

For Seattle, WA-based roles: The base salary range for this role is USD$171,000 per year - USD$190,000 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.