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As a Staff Engineer (5B) in Core Infrastructure, you will be a key technical leader responsible for the architecture and evolution of the systems that power Uber’s global business. You will engage with stakeholders across the company to lead the development of backend solutions that support every trip, delivery, and infrastructure tool used at Uber. We are moving to build a cloud-native by default ecosystem that is secure, reliable, and hyperscale-efficient. You will own the technical roadmap for critical sub-systems, ensuring they are prepared for 1M+ concurrent trips and the massive compute demands of Generative AI and Autonomous Vehicle data.
Job Responsibility
Lead Architectural Evolution: Identify architectural gaps in our compute and networking stacks
lead projects from ideation to global execution
Drive Efficiency at Scale: Design and implement solutions to increase fleet-wide CPU utilization and accelerate ARM adoption
Modernize for Multi-Cloud: Lead the technical strategy for Thrive in Cloud
Integrate AIOps & Automation: Drive the development of Agentic infrastructure tools to automate alert triaging and incident response
Champion Security by Design: Ensure 100% service-to-service authorization and zero-trust networking
Cross-Functional Influence: Collaborate with Storage, Data, and Product teams
Mentor & Scale Talent: Act as a force multiplier by mentoring 5A and mid-level engineers
Requirements
8+ years of full-time Software Engineering work experience (inclusive of PhD research or equivalent specialized education)
Deep proficiency in Go, Java, or C++
Demonstrated experience designing and productionizing large-scale, high-availability infrastructure services
A track record of leading complex, multi-quarter technical initiatives from design to fleet-wide rollout
Nice to have
Deep knowledge of Kubernetes internals, container runtimes (CRI), and networking (CNI/Envoy)
Prior experience with hybrid-cloud or multi-cloud migrations and cost-optimization at scale
Understanding of operating systems, Linux kernel performance tuning, or eBPF
Experience building or managing compute platforms tailored for GPU scheduling and large-scale model training
Contributions to open-source infrastructure projects or a history of presenting at major technical conferences