Machine Learning Engineer II
UberUnited States1 day ago
Full-timeEngineering, Information Technology
Track This Job
Add this job to your tracking list to:
- Monitor application status and updates
- Change status (Applied, Interview, Offer, etc.)
- Add personal notes and comments
- Set reminders for follow-ups
- Track your entire application journey
Save This Job
Add this job to your saved collection to:
- Access easily from your saved jobs dashboard
- Review job details later without searching again
- Compare with other saved opportunities
- Keep a collection of interesting positions
- Receive notifications about saved jobs before they expire
AI-Powered Job Summary
Get a concise overview of key job requirements, responsibilities, and qualifications in seconds.
Pro Tip: Use this feature to quickly decide if a job matches your skills before reading the full description.
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 ----
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 ----
- Support framing business and security problems as ML tasks.
- Build and iterate ML models that enable risk-adaptive, real-time decisions.
- Engineer features from Uber's risk systems, logs, and contextual signals.
- Deploy and maintain ML pipelines in production, ensuring reliability and scalability.
- Collaborate with senior engineers to integrate ML into Uber's authentication and authorization systems.
- 3+ years experience building and deploying ML models in production, with hands-on work in feature engineering, training, and evaluation.
- Proficiency in Python and ML frameworks (PyTorch, TensorFlow, or similar).
- Strong foundation in ML algorithms: tree-based models (XGBoost, LightGBM), classical methods (logistic regression, SVMs), and exposure to neural networks (CNNs, RNNs, Transformers).
- Ability to analyze business/security requirements and support translating them into ML use cases.
- Experience with risk, fraud, anomaly detection, or security-related ML systems.
- Familiarity with large-scale data/infra systems (Kafka, Hive, Spark, Flink, Pinot).
- Exposure to handling challenges such as imbalanced data, feedback loops, or iterative retraining.
- Strong communication skills and ability to work cross-functionally with infra, risk, and security teams.
Key Skills
Ranked by relevanceReady to apply?
Join Uber and take your career to the next level!
Application takes less than 5 minutes