Site Reliability Engineer – Automation and Platform

Staff Site Reliability Engineer – Automation and Platform

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach enables Cerebras to deliver industry-leading training and inference speeds and to help machine learning users run large-scale ML applications with reduced operational overhead.

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 and shared observability tooling. This role begins 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 Git Ops-driven CD for model releases, capacity provisioning, and cluster upgrades. Success over the first year 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 understand their pain points, automate toil, and mentor them as platform engineers.

You will collaborate with 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 let 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 heterogeneous clusters with a proprietary cloud control plane.
• Proven track record designing and delivering CI/CD or Git Ops 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.
• Prior work on predictive autoscaling, chaos engineering, or cost-aware capacity planning for compute-intensive workloads.

Location

• SF Bay Area
• Toronto

We are at the forefront of AI hardware and software innovation. Cerebras offers opportunities to work on a breakthrough AI platform and to contribute to a fast-growing team.

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Operate in a simple, non-corporate culture that respects individual beliefs.

Cerebras Systems is an equal opportunity employer. We celebrate diverse backgrounds, perspectives, and skills, and we are committed to creating an inclusive environment for all employees.