Staff Cloud Site Reliability Engineer

At Wayve we're committed to creating a diverse, fair and respectful culture that is inclusive of everyone based on their unique skills and perspectives, and regardless of sex, race, religion or belief, ethnic or national origin, disability, age, citizenship, marital, domestic or civil partnership status, sexual orientation, gender identity, veteran status, pregnancy or related condition (including breastfeeding) or any other basis as protected by applicable law. About us Founded in 2017, Wayve is the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward. Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving. In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future. At Wayve, your contributions matter. We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact. Make Wayve the experience that defines your career! The role 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. Key responsibilities 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. Ab ... (truncated, view full listing at source)