Senior Staff Engineer, Core Infrastructure

Uber Uber · Consumer · Aarhus, Denmark · Engineering

Senior Staff Engineer role focused on architecting and scaling Uber's core backend infrastructure, including deployment engines, autoscalers, and hybrid cloud environments. Responsibilities include improving quality, security, modernization, and efficiency, with a specific mention of GPU support for ML workloads as a preferred qualification.

What you'd actually do

  1. Architect and evolve backend infrastructure to support Uber’s growing workloads, including deployment engines, autoscalers, and hybrid cloud environments.
  2. Lead safe deployment and rollback automation across stateless, stateful, and batch workloads, improving resilience and developer efficiency.
  3. Improve infrastructure security and compliance, including encryption-at-rest, ransomware mitigation, and cloud security best practices.
  4. Advance Uber’s modernization efforts, including Kubernetes migration, unified workload platforms, and PaaS improvements.
  5. Optimize Uber’s infrastructure efficiency, focusing on ARM adoption, autoscaling enhancements, and cost-effective compute allocation.

Skills

Required

  • backend software development
  • distributed systems
  • infrastructure
  • cloud platforms
  • Go
  • Java
  • Kubernetes
  • high-scale systems

Nice to have

  • highly available cloud-native architectures
  • efficient cloud-native architectures
  • secure cloud-native architectures
  • safe deployment strategies
  • workload automation
  • resilience engineering
  • scaling autoscaling solutions
  • ARM adoption
  • hybrid cloud
  • GPU support for ML workloads
  • leading large technical initiatives
  • cross-team collaboration

What the JD emphasized

  • 10+ years of experience in backend software development with distributed systems, infrastructure, or cloud platforms.
  • Strong expertise in Go, Java, or similar backend languages, with a deep understanding of Kubernetes, cloud infrastructure, and high-scale systems.
  • Experience driving large-scale system modernization, performance optimizations, and deployment safety improvements.
  • Proven expertise in scaling autoscaling solutions, ARM adoption, hybrid cloud, or GPU support for ML workloads.