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Cerebras

AI accelerator hardware replacing GPUs

Distributed Software Engineer - Cluster engineering

Backend EngineeringConfirmed live in the last 24 hours
Full-timeToronto, ON, CanadaNo salary listed

About the Job

Requirements

  • At least 5 years of experience building and operating production distributed systems or infrastructure software.
  • Production-quality proficiency in Go and Python.
  • Deep Kubernetes experience, including writing or debugging controllers and operators and understanding custom resource definitions, reconciliation semantics, informer caches, admission webhooks, and role-based access control.
  • Strong debugging skills across distributed systems, Linux, and networking.
  • Practical Prometheus and Grafana experience, including Prometheus Query Language, exporter design, cardinality discipline, useful alerts, and service-level objectives.
  • Ability to work independently in a large, fast-moving, incompletely documented environment, build context, make decisions, and drive work across team boundaries.
  • Demonstrated adoption of artificial intelligence in the engineering workflow, including active use of coding agents, understanding where they help or mislead, and rigorously verifying their output.

Responsibilities

  • Build declarative, custom-resource-definition-driven automation for bare-metal networking, operating systems, and application software across clusters of Cerebras systems, servers, and switches, reconciling thousands of nodes.
  • Implement push-button cluster installation, upgrades, and security patching with real downtime budgets and canary gating.
  • Build Kubernetes operators that schedule large inference workloads using resource locks, priority queues, network topology, and health-aware placement.
  • Develop gRPC control-plane services, authorization, admission webhooks, and quota policies for a multi-tenant fleet.
  • Build metrics and log pipelines with purpose-built exporters for wafer-scale systems, servers using Redfish and IPMI, and network fabrics using gNMI and sFlow, on Prometheus and Grafana with service-level objectives and alerting.
  • Implement failure detection, highly available control planes, and automated recovery, along with command-line interfaces, application programming interfaces, and an MCP gateway that expose the fleet to users, operators, and AI agents.

Desired Qualifications

  • Experience with bare-metal or high-performance computing fleet operations.
  • Experience with scheduler internals.
  • Experience with RDMA/RoCE and eBPF networking.
  • Experience with Ceph or NVMe-oF.
  • Experience with etcd and highly available upgrades.
  • Experience with inference serving stacks.

About the Company

Cerebras

AI accelerator hardware replacing GPUs

Cerebras Systems creates AI acceleration hardware and software. Its CS-2 system is designed to replace traditional GPU clusters for AI workloads, speeding up training and inference while simplifying the setup by eliminating the need for parallel programming, distributed training, and cluster management. The product works as a single, large processor-based accelerator with accompanying software and cloud services to run AI models efficiently, reducing latency and time to results. Compared with competitors, Cerebras differentiates itself with the largest processor in the industry and an integrated hardware-software stack that aims to streamline AI workflows rather than relying on multi-GPU clusters. The company's goal is to help research labs, healthcare, finance, and other industries achieve faster, more cost-effective AI development and deployment by offering a turnkey high-performance AI compute solution.

Company size
501-1,000
Company stage
IPO
Headquarters
Sunnyvale, CA
Founded
2016

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