Staff Cloud SRE – AI/ML Platform & GPU Compute in London

About Wayve
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.

The role
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.

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.

About you
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.

Essential Skills

• 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 post‑mortems, and influencing teams to prioritise reliability improvements.

Working arrangement
This is a full‑time role based in our office in London (2 days a week in the office). At Wayve we operate a hybrid working policy that combines time together in our offices and workshops with time spent working from home.

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.

Equal Opportunity Employer
Wayve is 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. WAYVE is an equal opportunity employer. We will not ask about marriage or pregnancy, care responsibilities or disabilities in any of our job adverts or interviews. However, we do look to capture information about care responsibilities, and disabilities among other diversity information as part of an optional DEI Monitoring form to help us identify areas of improvement in our hiring process and ensure that the process is inclusive and non‑discriminatory.