Software Engineer II

Uber is looking for a strong backend and AI Software Engineer II to join the Data Security & Governance engineering team within the Privacy, Security & Risk org. This team builds distributed backend and AI-powered systems that operate at Uber scale, enabling engineering teams to safely adopt and innovate with ML and GenAI technologies. In this role, you will work on foundational platforms that protect Uber’s data and AI ecosystem. You’ll collaborate with senior engineers to design and build reliable, scalable systems, while gaining hands-on experience with production-grade distributed systems and AI-powered detection platforms., 1. As a Software Engineer II at Uber, you’ll be working on code that’s closest to the business.

1. You will build user-facing products, handle and store thousands of transactions per second, and develop intelligent Privacy (Data Deletion) and Security(Access Control) strategies that scale.
2. You will work on design, development and maintenance of services, frameworks, and solutions to support user-facing products, downstream services, or infrastructure tools and platforms used across Uber.
3. You are passionate and pragmatic technologists who are able to design scalable systems while delivering efficient code.
4. The privacy landscape is constantly evolving, and with the adoption of ML/AI backed by extensive heterogeneous data, security & privacy threats are inevitably broadened.
5. With your deep and comprehensive expertise across major technology stacks, you will design, implement and scale industry-leading privacy solutions for the entire company.

1. 4 years of professional industry experience and BS or MS in Computer Science or a related technical discipline, or equivalent practical experience.
2. Proficiency in at least one of Go, Java, Python, or C++.
3. Solid understanding of algorithms, data structures, and software engineering fundamentals.
4. At least five years of software engineering experience
5. Experience building or contributing to production backend or AI systems.
6. Experience with GenAI, Model Context Protocol, 1. Experience developing backend or distributed systems such as microservices, APIs, or data-processing services.
7. Familiarity with distributed systems concepts, including reliability, scalability, and performance trade-offs.
8. Exposure to ML or AI systems, including model inference, data pipelines, or ML-enabled features.
9. Interest or experience applying ML/AI techniques to security, privacy, or risk-related problems (e.g., data classification, PII detection, policy enforcement).
10. Experience with system monitoring, observability, and production operations.