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 top model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.
Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.
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
- Automate bare-metal configuration of networking, OS, and application software in large clusters of Cerebras WSE, servers, and switches.
- Additional push button workflows for cluster upgrades, downgrades, and security patching with key metrics to minimize downtime on clusters.
- An orchestration and scheduler system for resource allocation, job submission C placements for a multi-user environment on a cluster.
- Seamless support for both on-premise and cloud mode deployment and operations.
- A robust system for monitoring, detecting and handling failures for a variety of resources on the clusters (including High Availability of clusters).
- Broad cluster and job monitoring and visualization capabilities, along with alerting systems.
- User facing tools to monitor the status of jobs and collect metrics.
- Administrator facing tools to manage and operate large clusters.
Skills & Qualifications
- Strong track record of software architecture, system design and development.
- Strong track record of development in distributed cluster.
- Strong understanding of Kubernetes (K8s) software ecosystem, Prometheus and Grafana.
- Strong development skills in GoLang, Python, bash.
- Strong debugging skills with distributed systems.
- Strong skill to develop tests for the new features and regress old features.
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:
- 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 2026.
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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