Staff SRE, AI Infrastructure

Overview

Founding Staff SRE to build and scale Wayve’s AI cloud platform, including Model Development Platform and GPU Compute environments. You will define reliability foundations, automation, and standards to enable faster model training and reliable experimentation. You shape scalable, secure cloud infrastructure at the intersection of AI research and production operations. This is a ground-up role with a major impact on platform resilience and performance.

Pay / Benefits

• hybrid working policy (London office)
• office presence 2 days/week
• inclusive/intersectional culture

Responsibilities

• Own reliability, availability, and performance of Model Dev Platform and GPU Compute environments
• Define and operationalise SLOs, SLIs, and error budgets across platform services
• Improve capacity planning, scaling strategies, and resource efficiency for large GPU-backed clusters
• Partner with ML, platform, and software teams to establish production readiness standards
• Participate in 24/7 on-call rotation and lead incident triage, RCA, and post-incident improvements
• Design and operate observability systems, dashboards, and safe deployment practices
• Build automation for cluster operations, training workflows, remediation, and scaling tasks
• Harden CI/CD and release processes; support infrastructure-as-code and guardrails

Key requirements

• Proven experience in SRE/Production Engineering/Cloud Reliability for large-scale cloud systems
• Experience operating GPU-backed environments or large-scale ML infrastructure
• Experience running model training or inference pipelines in production (MLOps)
• Strong Kubernetes experience on production clusters
• Hands-on experience with AWS, GCP, or Azure
• Experience operating complex distributed systems, compute-heavy workloads
• Experience with large compute clusters; exposure to AI/ML workloads preferred
• Strong Linux fundamentals and scripting (Python, Go, C++) with automation bias
• Deep troubleshooting across networking, storage, distributed systems, and scale
• Experience designing observability stacks (Datadog, Prometheus, Grafana, OpenTelemetry)
• Clear communication skills including incident leadership and postmortems

• Clear communication
• Cross-functional collaboration
• Bias toward automation
• Kubernetes production clusters
• Cloud platforms: AWS, GCP, Azure
• MLOps for model training/inference pipelines

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