Senior Cloud Site Reliability Engineer; AI​/ML Platform & GPU Compute

• As a 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 ope rationalise 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

• Private healthcare:
  Choose our optional health insurance for comprehensive coverage for you and your family.
• Paid time off:
  Paid vacation plus public holidays and additional leave programs, ensuring you have time to unwind.
• Mental health resources:
  Through Spill, you can access therapy and mental health support.
• Community and socials:
  Join clubs or attend team socials to connect over hobbies, sports, or just for fun.
• Competitive compensation:
  Our compensation package includes cash and equity, making you a true partner in our success.
• Learning and development:
  Budgets for books, courses, and company-wide training to support your continuous growth.

In order to set you up for success as a Cloud Site Reliability Engineer at Wayve, we’re looking for the following skills and experience

Experience operating complex distributed systems in production, ideally including compute-heavy or high-performance workloads

Strong Linux fundamentals and proficiency in at least one scripting or systems language (e.g. Python, Go, C++) with a bias toward automation

Strong Kubernetes experience, including operating production clusters

Deep troubleshooting skills across networking, storage, distributed systems, and performance at scale

Hands-on experience running production workloads in AWS, GCP, or Azure Experience designing and operating observability stacks (e.g. Datadog, Prometheus, Grafana, Open Telemetry)
Clear communication skills, including leading incidents, writing postmortems, and influencing teams to prioritise reliability improvements

Proven experience in an SRE, Production Engineer, or Cloud Reliability role supporting large-scale cloud systems

Experience working with large compute clusters; exposure to AI/ML training or inference workloads strongly preferred

Experience operating GPU-backed environments or large-scale ML infrastructure

Experience running model training or inference pipelines in production (MLOps)
Familiarity with infrastructure-as-code (e.g. Terraform) and secure cloud production environments

Experience defining and running SLOs/SLIs and building reliability programs across multiple teams

Experience as an early or founding SRE hire establishing processes from scratch

Interest in helping shape and grow a Cloud SRE function, with potential to take on leadership responsibilities over time