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 global corporations across multiple industries, national labs, and top-tier healthcare systems. In January, we announced a multi-year, multi-million-dollar partnership with Mayo Clinic, underscoring our commitment to transforming AI applications across various fields. In August, we launched Cerebras Inference, the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

About The Role

We are seeking a highly skilled and experienced AI Infrastructure Operations Engineer to manage and operate our cutting-edge machine learning compute clusters. These clusters would provide the candidate 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

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.
Deploy, configure, and debug container-based services using Docker.
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.
Contribute to the development and improvement of our monitoring and support processes.
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.
Strong proficiency in Python scripting for automation and system administration.
Deep understanding of Linux-based compute systems and command-line tools.
Extensive knowledge of Docker containers and container orchestration platforms like k8s and SLURM.
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 large scale GPU clusters.
Knowledge of technologies like Ethernet, RoCE, TCP/IP, etc. is desired.
Knowledge of cloud computing platforms (e.g., AWS, GCP, Azure).
Familiarity with machine learning frameworks and tools.
Experience with cross-functional team projects.

Location

SF Bay Area.
Toronto, Canada.
Bangalore, India.

AI Infrastructure Operations Engineer

Job Type