Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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 works with the leading model labs, global enterprises, and cutting-edge AI-native startups.

The Role The

Cluster engineering team owns the software that turns thousands of wafers, servers, and switches into a cloud that stays up, stays busy, and stays debuggable. We stand clusters up from bare metal, schedule training and inference workloads across the fleet, keep it healthy, and make it observable to users, operators, and increasingly to AI agents. The stack is Go and Python on Kubernetes, running both on-premise deployments and our own cloud.

Declarative, CRD-driven automation of bare-metal networking, OS, and application software across clusters of Cerebras systems, servers, and switches, built to reconcile thousands of nodes

Kubernetes operators that schedule large inference workload: resource locks, priority queues, network topology, and health-aware placement gRPC control-plane services, authorization, admission webhooks, and quota policy for a multi-tenant fleet

Metrics and log pipelines with purpose-built exporters for wafer-scale systems, servers (Redfish, IPMI), and network fabric (gNMI, sFlow), on Prometheus and Grafana, with SLOs and alerting

Failure detection, HA control planes,



and automated recovery, plus the CLIs, APIs, and MCP gateway that expose the fleet to users, operators, and AI agents

5+ years building and operating production distributed systems or infrastructure software

Production-quality Go and Python

Real Kubernetes depth: you have written or debugged controllers and operators, and you understand CRDs, reconciliation semantics, informer caches, admission webhooks, and RBAC

Strong debugging skills across distributed systems, Linux, and networking

Demonstrated adoption of AI in your engineering workflow: active use of coding agents, a view on where they help and where they mislead, and the rigor to verify what they produce.

Nice to have: bare-metal or HPC fleet operations, scheduler internals, RDMA/RoCE and eBPF networking, Ceph or NVMe-oF, etcd and HA upgrades, inference serving stacks. ML research experience is not required.

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. 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. Our easy, non-corporate work culture that respects individual beliefs.

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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📌 Software Deployment Specialist (Toronto)
🏢 Cerebras
📍 Toronto