Staff Cloud SRE – AI/ML Platform & GPU Compute

Salary: £60,000 - 60,000 per year

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.

Responsibilities:

• Own the reliability, availability, and performance of our 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, not just infrastructure metrics.
• 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.

Technologies:

• AI
• AWS
• Azure
• CI/CD
• Cloud
• Datadog
• GCP
• Grafana
• Hardware
• Support
• Kubernetes
• Linux
• MLOps
• Model Training
• OpenTelemetry
• Prometheus
• Python
• Terraform
• DevOps

More:

Founded in 2017, we are the leading developer of Embodied AI technology. Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate complex environments, improving the usability and safety of automated driving systems. Our vision is to create autonomy that propels the world forward, and our intelligent, mapless, hardware-agnostic AI products help automakers accelerate the transition from assisted to automated driving. This is a founding Staff Cloud Site Reliability Engineer role based in our London office, with a hybrid working policy of 2 days a week in the office. We foster a fast-paced, inclusive environment where big problems, continuous learning, and collaboration drive impact. We are committed to diversity, respect, and creating an interview experience that is inclusive and accessible.

last updated 35 week of 2026