Site Reliability Engineer - Ops & Automation

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), helping deliver infrastructure for frontier-class models from leading model builders such as OpenAI. This role offers immediate ownership of real production systems at a growing scale, direct mentorship from seasoned engineers, and close collaboration with incoming Staff SREs who will focus on long-term automation. After ~1 month of shared hands-on operations with the Staff engineers, you'll primarily operate the current setup, bring up new capacity in high-stakes environments and help bring new continuous delivery pipelines into production use. If you thrive in high-ownership SRE roles at scale and want to help shape a team from the ground up in cutting-edge AI Inference infrastructure, this is your chance. This role does not require 24/7 on-call rotations.

Responsibilities

• Remain hands-on with operational execution (releases, capacity changes, cluster upgrades) over the next year as we build robust continuous delivery pipelines and self-service capabilities
• Contribute to the development of self-service CD pipelines for key workflows using our stack: Kubernetes, Bazel, Prometheus/Grafana/InfluxDB, Python, and Go.
• Build reusable automation and internal developer tools that minimize operational toil and cross-team friction
• Develop and extend telemetry, observability and alerting solutions to ensure operational reliability at scale
• Collaborate with Cluster Ops and development teams to identify high-impact automation opportunities and iterate quickly
• Contribute to reliability practices (SLOs, post-mortems, capacity planning)
• Required Experience & Skills
• 2-4+ years in SRE with a strong operations or automation focus
• Production Kubernetes experience
• Solid Python or Go for building tools and automation
• Proficiency with Prometheus, Grafana, and observability-driven workflows
• Ability to measure and communicate impact - reliability metrics, operational toil, velocity gains
• Nice-to-Ha ve
• Hands-on GitOps expertise, Argo CD / Flux or equivalent, is a plus
• Experience with building continuous delivery pipelines is a strong plus
• Experience with Bazel or similar build systems is a strong plus.
• Familiarity with capacity planning, on-prem or multi-datacenter environments
• Location
• SF Bay Area
• Toronto
• Why Join Cerebras
• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source their cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Our simple, non-corporate work culture that respects individual beliefs.
• Read our blog: Five Reasons to Join Cerebras in 2026.
• Apply today and become part of the forefront of groundbreaking advancements in AI!
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Additional Information

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel 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 allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top 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. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.

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