Staff Site Reliability Engineer – Automation and Platform

About the role

We are building a high-performance SRE function to support one of the world’s fastest-growing AI inference services, powered by the Wafer-Scale Engine (WSE). This team will help deliver world-class, ultra-reliable inference infrastructure for leading model builders such as OpenAI and other frontier labs.

Key Responsibilities

• Define and implement a robust strategy for delivering and running software reliably and at scale across multiple datacenters and cloud-based solutions.
• Architect self-service platforms and internal tooling that lets product teams, external customers, and cluster operators safely trigger and observe critical workflows with minimal handoffs.
• Define and evolve reliability practices for inference workloads, including SLOs and SLIs for latency, throughput, and accuracy stability; error budgets; blameless postmortems; chaos testing; and capacity forecasting across multi-datacenter and on-prem environments.
• Mentor mid-level SREs, support critical incident escalations, and use production pain points to prioritize the highest-leverage automation work.
• Measure and drive impact through clear metrics, including toil reduction, deployment velocity, SLO compliance, MTTR, and adoption of self-service workflows.

Required Experience & Skills

• 8+ years in SRE, infrastructure engineering, or platform engineering, with a strong record of improving automation and reliability at large scale in FAANG, hyperscaler, or similarly demanding environments.
• Deep expertise operating large scale heterogenous clusters with a proprietary cloud control plane.
• Proven track record designing and delivering CI/CD or GitOps systems using Argo CD or similar tools, with strong safety and observability built in.
• Hands-on experience with observability systems such as Loki, Tempo, Mimir, and Prometheus.
• Able to lead complex projects end to end, influence cross-functional stakeholders, and communicate technical direction clearly.

Nice-to-Haves

• Experience with Bazel or other large-scale build systems in production.
• Background in AI/ML inference systems, including model serving runtimes, GPU or wafer-scale orchestration, latency and accuracy SLOs, or drift monitoring.
• Prior work on predictive autoscaling, chaos engineering, or cost-aware capacity planning for compute-intensive workloads.

Why Join Cerebras

Cerebras is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies.