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
Cerebras Systems is a pioneer in large-scale AI Supercomputers. These multi-exaflop supercomputers are deployed in some of the biggest datacenters. These supercomputers are built using our Wafer-Scale Cluster technology - a cluster of several Wafer Scale Engine (WSE) chips. The Cluster engineering team is responsible for delivering software that are all-things related to cluster.
Responsibilities
- Assist in automating the configuration of networking, OS, and application software for large clusters of Cerebras WSE, servers, and switches.
- Contribute to building workflows for cluster upgrades, downgrades, and security patching, with a focus on minimizing downtime.
- Support the development of orchestration and scheduling systems for resource allocation, job submission, and placement in a multi-user cluster environment.
- Help ensure seamless deployment and operations for both on-premise and cloud-based clusters.
- Contribute to monitoring systems that detect and handle failures across various cluster resources, including High Availability configurations.
- Assist in building broad cluster and job monitoring tools, along with alerting systems.
- Develop user-facing tools for monitoring job status and gathering metrics.
- Create administrator-facing tools to help manage and operate large-scale clusters effectively.
Skills And Qualifications
- Enrolled in a university program with a degree in Computer Science, Computer Engineering, or other related disciplines.
- Solid understanding of software architecture, system design, and development principles.
- Familiarity with development in distributed cluster environments.
- Basic knowledge of Kubernetes (K8s), Prometheus, and Grafana.
- Proficiency in at least one programming language such as Go, Python, or bash.
- Strong problem-solving and debugging skills.
- Ability to write tests for new features and ensure existing features are properly tested.
- A real passion for AI!
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:
- 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 2025.
Apply today and become part of the forefront of groundbreaking advancements in AI!
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.
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