Staff SRE, AI Infrastructure
Full-time
Wayve
Before the detail, here's the challenge you'd help us solve. We build the embodied intelligence that moves real vehicles safely, and the ecosystem a billion machines will run on in the future. Very few people in AI can say this. Every role here, whatever the team, plugs into that. Here’s what this particular role covers. ️ About Our AI Infrastructure Teams Our AI Infrastructure teams build and operate the platforms that power Wayve’s AI development. Working across AI Platform, Compute & Data Platform, and Cloud Infrastructure, we provide reliable access to large-scale GPU compute, Kubernetes, storage, and deployment systems. As our GPU fleet and data footprint grow rapidly, you’ll help establish the reliability standards, automation, and operating practices needed to support that scale. Your day-to-day You’ll work hands-on with engineers across our infrastructure teams to identify reliability risks and turn recurring operational problems into durable engineering solutions. You’ll improve observability, automation, deployment safety, capacity planning, and incident learning while helping teams adopt effective SLOs and production-readiness practices. You’ll also troubleshoot complex failures spanning compute, networking, storage, and distributed workloads. What You’ll Be Working On You’ll improve GPU node health detection and automated recovery, strengthen the resilience of distributed AI training, and develop continuous GitOps delivery using platforms such as Argo CD or Flux. You’ll enhance Kubernetes scheduling, autoscaling, policy enforcement, and resource efficiency; build forecasting for GPU and storage capacity; and create reusable self-service capabilities that help platform teams operate reliably at scale. You should apply if You have hands-on experience owning the reliability of large-scale cloud, Kubernetes, or distributed infrastructure and have recently coded, automated, configured, or troubleshot production systems yourself. You’re comfortable working in Python or Go, infrastructure as code, CI/CD or GitOps, and modern observability practices. You understand distributed-systems failure modes across compute, networking, and storage, can demonstrate measurable improvements to reliability, performance, capacity, or cost, and collaborate effectively across teams. Direct experience with GPU, ML-training, or HPC infrastructure is valuable, but strong experience solving comparable large-scale platform challenges is also relevant. Not ticking every box? That’s totally okay! If you’re passionate about autonomy and keen to learn, we encourage you to apply even if you don’t meet every requirement. More About Wayve Wayve is building the leading AI platform for autonomous driving. We are pioneering an end to end AI approach that enables vehicles to learn directly from real world experience, developing the ability to adapt, generalise and improve at scale. Instead of relying on hand coded rules or pre mapped environments, our AI Driver learns to drive by understanding the world around it. The result is technology that navigates complex urban environments with intelligence, precision and natural flow, unlocking meaningful advances in both safety and efficiency. We believe autonomy represents a once in a generation transformation in how people and goods move, comparable to the shift from horses to cars, and from human driven vehicles to intelligent machines. Our ambition is to make autonomy universal. Wayve’s mapless and hardware agnostic AI platform integrates with global OEM partners, enabling continuous software evolution and unlocking advanced levels of automation from L2 plus through to L4 as our core AI model scales. In a race increasingly defined by intelligence and real world learning, Wayve is taking a distinct approach, building a generalisable driving intelligence that can power any vehicle, anywhere. By combining embodied AI with scalable deployment, we are creating technology that can be shaped to each OEM brand and driver experience, accelerating the transition to a safer, more intelligent future of mobility. How we work - Locations & Flexible Working: Our main hubs are in London, Sunnyvale, Yokohama, Herzliya, Vancouver and Leonberg. We operate a hybrid working model that combines in-person collaboration in our dedicated office spaces with focused time working remotely. This gives our teams the connection and energy of working together, alongside the flexibility to do their best work in a way that fits their lives. The Interview Process Our process is clear and respectful of your time:
- Initial call / recruiter screen
- Competency Interviews
- Deep-dive technical interviews
- Final interview: mission & values alignment
Vacancy posted 2 days ago
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