Staff SRE, AI Infrastructure

At Wayve, we’re building a global driving intelligence that learns from data and scales across different vehicles and geographies.

Founded in 2017, we have pioneered an end-to-end AI approach to autonomous driving that is faster to deploy, more flexible by design and built to scale. We deliver all levels of autonomy, from hands-off and eyes-off driving, to robotaxis. We license and integrate the Wayve AI Driver as a vehicle-agnostic software platform that runs entirely on onboard vehicle compute and native sensors.

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.

• 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.
• 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.
• 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.
• 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.

• 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.

• 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.