Uber
Staff Software Engineer - Core Infrastructure
$232K–$258K
San Francisco, California +1 · Staff · Full-time · Posted Oct 19, 2026
- Location
- San Francisco, California
Sunnyvale, California - Experience
- 8+ years of full-time Software Engineering work experience, inclusive of PhD research or equivalent specialized education.
- Education
- Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field (or equivalent practical experience).
About the job
Leads architecture and evolution of Uber’s global core infrastructure, including compute, networking, multi-cloud resilience, efficiency, and automation.
What you'll do
- Lead infrastructure architecture projects from design through global rollout, including compute and networking improvements.
- Drive fleet efficiency, multi-cloud resilience, AIOps automation, and security-by-design initiatives.
- Collaborate across teams and mentor engineers to support large-scale workloads and operational excellence.
What they ask for
- Bachelor’s degree in Computer Science, Computer Engineering, or a closely related technical field (or equivalent practical experience).
- Deep, hands-on proficiency in Go, Java, or C++.
- Demonstrated experience designing, building, and productionizing large-scale, high-availability infrastructure services.
- Track record leading complex, multi-quarter technical initiatives from design through fleet-wide rollout.
Nice to have
- Deep knowledge of Kubernetes internals, container runtimes (CRI), and modern networking (CNI/Envoy).
- Experience with hybrid-cloud or multi-cloud migrations, Linux/kernel performance tuning, GPU infrastructure, or open-source contributions.
Pay
USD $232,000 per year - USD $258,000 per year (base salary; San Francisco, CA); USD $232,000 per year - USD $258,000 per year (base salary; Sunnyvale, CA)
Tools
Go, Java, C++, Kubernetes, CRI, CNI, Envoy, eBPF
AI focus
Develops agentic infrastructure tools for automated alert triage and incident response, and infrastructure to support GPU scheduling, large-scale model training, and Generative AI workloads.
Model-written summary · the original posting is authoritative