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Distributed Software Engineer

India, Bengaluru · Job Posted February 17, 2026
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Job Description

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. 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.

Job Responsibility

  • 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

Requirements

  • 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

What we offer

  • 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

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