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Wayve is building the leading AI platform for autonomous driving, pioneering an end-to-end AI approach that enables vehicles to learn directly from real-world experience. The company operates a hybrid model across hubs in London, Sunnyvale, Yokohama, Herzliya, Vancouver, and Leonberg.
As a Staff SRE for AI Infrastructure, you will work hands-on with engineers across infrastructure teams to identify reliability risks and transform recurring operational problems into durable engineering solutions. Your focus will span AI Platform, Compute & Data Platform, and Cloud Infrastructure, supporting large-scale GPU compute, Kubernetes, storage, and deployment systems.
Day-to-day responsibilities include:
- Improving observability, automation, deployment safety, capacity planning, and incident learning
- Helping teams adopt effective SLOs and production-readiness practices
- Troubleshooting complex failures spanning compute, networking, storage, and distributed workloads
- Improving GPU node health detection and automated recovery
- Strengthening the resilience of distributed AI training systems
- Developing continuous GitOps delivery using platforms such as Argo CD or Flux
- Enhancing Kubernetes scheduling, autoscaling, policy enforcement, and resource efficiency
- Building forecasting for GPU and storage capacity
- Creating reusable self-service capabilities for platform teams to operate reliably at scale
You will demonstrate measurable improvements to reliability, performance, capacity, or cost, and collaborate effectively across teams to establish the reliability standards, automation, and operating practices needed to support rapid growth in GPU fleet and data footprint.
Wayve emphasizes that this is not a mature, fully-structured organization—much of how they work is still being written, and successful candidates will help shape it. The role suits people who want real ownership and are comfortable with evolving processes.
REQUIREMENTS:
- Hands-on experience owning the reliability of large-scale cloud, Kubernetes, or distributed infrastructure
- Recent hands-on coding, automation, configuration, or troubleshooting of production systems
- Proficiency in Python or Go
- Experience with infrastructure as code, CI/CD or GitOps, and modern observability practices
- Understanding of distributed-systems failure modes across compute, networking, and storage
- Ability to demonstrate measurable improvements to reliability, performance, capacity, or cost
- Strong cross-team collaboration skills
- Direct experience with GPU, ML-training, or HPC infrastructure is valuable but not required; strong experience solving comparable large-scale platform challenges is also relevant
- Passion for autonomy and willingness to learn are encouraged even if not meeting every requirement
