Cluster Operations Software Engineer

Cerebras Systems; Toronto; On-site; CAD 120,000 - 160,000; Full time

Job summary

Cerebras Systems is seeking an AI Cluster Operations Engineer to manage and operate our Wafer-Scale Engine compute clusters, ensuring health, performance, and availability to support cutting-edge AI workloads.

You will develop automation, monitoring dashboards, and reliability tooling, work with Linux-based systems, Docker and Kubernetes, and provide 24/7 on-call support across global infrastructure to maximize compute capacity.

Qualifications

• 6-8 years of experience managing and operating complex compute infrastructure.
• Proficiency in Python and Go for building operational platforms.
• Experience with distributed systems and Linux-based compute environments.
• Extensive knowledge of Docker and container orchestration (Kubernetes).

Responsibilities

• Deploy, configure, and debug container-based services using Docker.
• Build and own software for cluster operations including monitoring platforms and reliability tooling.
• Collaborate with cross-functional teams to translate requirements into scalable O&M products.
• Develop APIs, automation services, and integrations for global AI infrastructure.
• Monitor cluster health and maximize compute capacity.
• Provide 24/7 monitoring and hands-on troubleshooting.

Skills

Python

Go

Distributed systems

Linux

Docker

Kubernetes

Job description

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.

• Toronto, Canada.

• Bangalore, India.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

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

Find out more about what it\'s like to work at Cerebras here.

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.