Senior AI/ML Engineer
Indexed description
We are responsible for providing and managing systems, services, and libraries to provide access management, and enforcement at scale. The scope spans across multiple verticals like service-to-service authentication/authorization, employee to system auth, customer auth.
You'll work on critical distributed services at a massive scale crafted with the best security practices at the forefront. You'll be accountable for designing and implementing the AI/ML based solutions to continuously scale and operate such foundational security services.
What the Candidate Will Do ----
- Translate business and security needs into well-defined problem statements and solve them with the AI-first mindset.
- Develop, iterate, and productionize ML models that simplify access management and control.
- Integrate ML systems into Uber's critical systems (Identity, Access, Authorization).
- 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 Secury.
- 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.
- 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.
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