Cluster Operations Software Engineer

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 https://openai.com/index/cerebras-partnership/ with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference. THE ROLE We are seeking a highly skilled and experienced AI Cluster Operations Engineer to manage and operate our cutting-edge machine learning compute clusters. These clusters would provide the candidate with an opportunity to work with the world's largest computer chip, the Wafer-Scale Engine (WSE), and the systems that harness its unparalleled power. You will play a critical role in ensuring the health, performance, and availability of our infrastructure, maximizing compute capacity, and supporting our growing AI initiatives. This role requires a deep understanding of Linux-based systems, containerization technologies, and experience with monitoring and troubleshooting complex distributed systems. The ideal candidate is a proactive problem-solver with expertise in large-scale compute infrastructure, dependable and an advocate for customer success. RESPONSIBILITIES - Deploy, configure, and debug container-based services using Docker. - Build and own software solutions that power cluster operations, including monitoring platforms, workflow automation systems, operational dashboards, and reliability tooling. - Collaborate with cross-functional teams to translate operational requirements into scalable O&M products and platform capabilities. - Develop APIs, automation services, and integrations that improve operational visibility, incident response, and fleet management across global AI infrastructure. - Manage and operate multiple advanced AI compute infrastructure clusters. - Monitor and oversee cluster health, proactively identifying and resolving potential issues. - Maximize compute capacity through optimization and efficient resource allocation. - Provide 24/7 monitoring and support, leveraging automated tools and performing hands-on troubleshooting as needed. - Handle engineering escalations and collaborate with other teams to resolve complex technical challenges. - Stay up-to-date with the latest advancements in AI compute infrastructure and related technologies. SKILLS AND REQUIREMENTS - 6-8 years of relevant experience in managing and operating complex compute infrastructure, preferably in the context of machine learning or high-performance computing. - Proficient in Python and Go, with experience building operational platforms, workflow automation systems, and reliability tooling for large-scale infrastructure environments. - Experience and Expertise in distributed systems is a must. - Deep understanding of Linux-based compute systems and command-line tools. - Extensive knowledge of Docker containers and container orchestration platforms like k8s. - Proven ability to troubleshoot and resolve complex technical issues in a timely and efficient manner. - Experience with monitoring and alerting systems. - Should have a proven track record to own and drive challenges to completion. - Excellent communication and collaboration skills. - Ability to work effectively in a fast-paced environment. - Willingness to participate in a 24/7 on-call rotation. PREFERRED SKILLS AND REQUIREMENTS - Operating and Managing large scale AI clusters. - Knowledge of technologies like Ethernet, RoCE, TCP/IP, etc. is desired. - Knowledge of cloud computing platforms (e.g., AWS, GCP, Azure). LOCATION - SF Bay Area. -…