This list contains only the countries for which job offers have been published in the selected language (e.g., in the French version, only job offers written in French are displayed, and in the English version, only those in English).
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