C

Cerebras

AI accelerator hardware replacing GPUs

Cluster Operations Software Engineer

Site Reliability EngineeringConfirmed live in the last 24 hours
Full-timeNo salary listed

About the Job

Requirements

  • Six to eight years of relevant experience managing and operating complex compute infrastructure, preferably in machine learning or high-performance computing.
  • Proficiency in Python and Go, with experience building operational platforms, workflow automation systems, and reliability tooling for large-scale infrastructure environments.
  • Expertise in distributed systems.
  • Deep understanding of Linux-based compute systems and command-line tools.
  • Extensive knowledge of Docker containers and container orchestration platforms such as Kubernetes.
  • Ability to troubleshoot and resolve complex technical issues in a timely and efficient manner.
  • Experience with monitoring and alerting systems.
  • Proven track record of owning and driving challenges to completion.
  • Ability to work effectively in a fast-paced environment.
  • Willingness to participate in a 24/7 on-call rotation.

Responsibilities

  • Deploy, configure, and debug container-based services using Docker.
  • Build and own software solutions for cluster operations, including monitoring platforms, workflow automation systems, operational dashboards, and reliability tooling.
  • Collaborate with cross-functional teams to translate operational requirements into scalable operations and maintenance products and platform capabilities.
  • Develop APIs, automation services, and integrations to improve operational visibility, incident response, and fleet management across global AI infrastructure.
  • Manage and operate multiple advanced AI compute infrastructure clusters.
  • Monitor cluster health, proactively identifying and resolving potential issues.
  • Maximize compute capacity through optimization and efficient resource allocation.
  • Provide 24/7 monitoring and support, using automated tools and hands-on troubleshooting as needed.
  • Handle engineering escalations and collaborate with other teams to resolve complex technical challenges.
  • Stay current with advancements in AI compute infrastructure and related technologies.

Desired Qualifications

  • Experience operating and managing large-scale AI clusters.
  • Knowledge of Ethernet, RoCE, and TCP/IP.
  • Knowledge of cloud computing platforms such as AWS, GCP, and Azure.
  • Experience with machine learning or high-performance computing infrastructure.

About the Company

Cerebras

AI accelerator hardware replacing GPUs

Cerebras Systems creates AI acceleration hardware and software. Its CS-2 system is designed to replace traditional GPU clusters for AI workloads, speeding up training and inference while simplifying the setup by eliminating the need for parallel programming, distributed training, and cluster management. The product works as a single, large processor-based accelerator with accompanying software and cloud services to run AI models efficiently, reducing latency and time to results. Compared with competitors, Cerebras differentiates itself with the largest processor in the industry and an integrated hardware-software stack that aims to streamline AI workflows rather than relying on multi-GPU clusters. The company's goal is to help research labs, healthcare, finance, and other industries achieve faster, more cost-effective AI development and deployment by offering a turnkey high-performance AI compute solution.

Company size
501-1,000
Company stage
IPO
Headquarters
Sunnyvale, CA
Founded
2016

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