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About 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. You will build and scale the reliability foundations of our AI cloud platform, defining the frameworks, automation, and operational standards that ensure our infrastructure operates predictably, efficiently, and at scale. Your work will directly enable faster model training, reliable experimentation, and scalable AI deployment.
Responsibilities
- 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.
- 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.
Requirements
- 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.
Skills
- AI
- GPU compute infrastructure
- Cloud platform reliability
- Model Development Platform
- GPU Compute platform
- SLOs
- SLIs
- Error budgets
- Capacity planning
- Scaling strategies
- Resource efficiency
- Production readiness
- Incident response
- On-call
- Triage
- Escalation
- Root cause analysis
- Observability
- Monitoring
- Logging
- Tracing
- Alerting
- Dashboards
- Change management
- Deployment safety
- Rollback mechanisms
- Automation
- Tooling
- Cluster operations
- Training workflows
- Remediation
- Self-healing patterns
- Resilient recovery
- CI/CD
- Release processes
- Infrastructure-as-code
- Policy-driven guardrails
- Kubernetes
- AWS
- GCP
- Azure
- Distributed systems
- Linux
- Python
- Go
- C++
- Networking
- Storage
- Performance at scale
- Datadog
- Prometheus
- Grafana
- OpenTelemetry
- Terraform
Location
- London
Work Type
- Full-time
- Hybrid
Experience Level
- Staff
- Founding
About the Company
- Wayve is a company focused on AI systems and GPU compute infrastructure.