Staff Site Reliability Engineer – Automation and Platform

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services. This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation. Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

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.

As a Staff SRE, you will lead the engineering effort to eliminate toil at scale by driving implementation of self-service delivery pipelines, shared observability common tooling. This role starts with ~1 month of hands-on operational immersion to gain deep familiarity with our current stack, production pain points, and high-stakes workflows.

From there, your primary focus shifts to architecting and delivering the "tomorrow" layer: declarative GitOps-driven CD for model releases, capacity provisioning and cluster upgrades. Success over the first year in this role will be defined by enabling core teams, product managers, external customers, and cluster stakeholders to operate in a fully self-service model with strong reliability guarantees.

You will partner with our early-career SRE sub-team, who own day-to-day operations. This will allow you to deeply understand their pain points, automate their toil, and mentor them as platform engineers.

You will collaborate with the tech leads and the leadership team across core, cluster, cloud, and product stakeholders. This work will shift reliability from an ops-only burden to a shared engineering discipline that underpins frontier AI inference at scale.

If you are a proven Staff+ engineer who enjoys turning complexity into elegant reliability at scale, this is your chance to lead this transformation from the front.

This role does not require 24/7 on-call rotations.

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

Ability 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.