Staff Cloud Site Reliability Engineer (AI/ML Platform & GPU Compute) in London

Requirements

• Proven experience in an SRE, Production Engineer, or Cloud Reliability role supporting 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, including operating production clusters
• Hands-on experience running production workloads in AWS, GCP, or Azure
• Experience operating complex distributed systems in production, ideally including compute-heavy or high-performance workloads
• Experience working with large compute clusters; exposure to AI/ML training or inference workloads strongly preferred
• Strong Linux fundamentals and proficiency in at least one scripting or systems language (e.g. Python, Go, C++) with a bias toward automation
• Deep troubleshooting skills across networking, storage, distributed systems, and performance at scale
• Experience designing and operating observability stacks (e.g. Datadog, Prometheus, Grafana, OpenTelemetry)
• Clear communication skills, including leading incidents, writing postmortems, and influencing teams to prioritise reliability improvements
• (Desirable) Familiarity with infrastructure-as-code (e.g. Terraform) and secure cloud production environments
• (Desirable) Experience defining and running SLOs/SLIs and building reliability programs across multiple teams
• (Desirable) Experience as an early or founding SRE hire establishing processes from scratch
• (Desirable) Interest in helping shape and grow a Cloud SRE function, with potential to take on leadership responsibilities over time

We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it takes to make a positive impact on the world, we encourage you to apply.

What the job involves

This is a rare opportunity to be a founding Staff SRE shaping the reliability of large-scale AI systems and GPU compute infrastructure from the ground up. As a Staff Cloud Site Reliability Engineer at Wayve, you will build and scale the reliability foundations of our AI cloud platform. This includes our Model Development Platform (powering end-to-end model development from raw data to on-road experimentation) and our GPU Compute platform (large-scale, multi-tenant GPU fleets and scheduling systems driving model training and inference at scale).

This is a founding Cloud SRE role. You won’t inherit a mature SRE function, you’ll help create it. You will define the frameworks, automation, and operational standards that ensure our model development infrastructure, distributed systems, and large compute clusters operate predictably, efficiently, and at scale. This role sits at the intersection of AI research, large-scale cloud infrastructure, and production operations. Your work will directly enable faster model training, reliable experimentation, and scalable AI deployment by ensuring our cloud infrastructure is resilient and performant.

Reliability & Platform Ownership

• Own the reliability, availability, and performance of the 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 across large GPU-backed clusters
• Partner with ML, platform, and software teams to establish clear production readiness standards

Incident Response & On-Call

• Participate in a 24/7 on-call rotation as first-line response for cloud and cluster-related incidents
• Lead incident triage, escalation, communications, and root cause analysis
• Translate post-incident learning into durable architectural or automation improvements
• Continuously reduce alert noise and recurring operational burden

Observability & Operational Excellence

• Design and operate monitoring, logging, tracing, and alerting systems that enable rapid detection and recovery
• Build dashboards that reflect real user-centric platform health (not just infrastructure metrics)
• Improve deployment safety through better change management, validation, and rollback mechanisms

Automation & Tooling

• Build automation for cluster operations, training workflows, remediation, and scaling tasks
• Implement self-healing patterns and resilient recovery workflows
• Harden CI/CD and release processes to improve deployment safety and velocity
• Support infrastructure-as-code and policy-driven guardrails to ensure secure, reliable cloud environments