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Distributed Software Engineer

Cerebras· Software Engineering
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Hybrid (Toronto, CAN)Full-timePosted 1mo ago

About the role

Role focused on clustering, automation, and orchestration for Cerebras system clusters, including bare-metal provisioning, Kubernetes-based workload scheduling, and multi-tenant control planes across on-premise and Cerebras cloud environments.

Responsibilities

  • 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
  • Push-button cluster install, upgrade, and security patching with real downtime budgets, gated by canaries
  • 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

Qualifications

  • 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, you understand CRDs, reconciliation semantics, informer caches, admission webhooks, and RBAC
  • Strong debugging skills across distributed systems, Linux, and networking
  • Prometheus and Grafana as a practitioner: PromQL, exporter design, cardinality discipline, useful alerts
  • Strong self-driving capability. This environment is large, fast-moving, and not fully documented, so we need engineers who build their own context, decide, and drive work across team boundaries. Learning speed matters more here than familiarity with our stack.
  • 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.

Skills

Required:GoPythonKubernetesLinuxPrometheus & Grafana
Last updated Sep 13, 2026. Interested? Apply on the company site.

About Cerebras

Cerebras is the go-to platform for fast and effortless AI training. Learn more at cerebras.ai.

Cerebras is the go-to platform for fast and effortless AI training.

AICerebras Wafer-Scale Enginechipshardwareinferenceliquid-ai-recruiting-targetmultimodalsetter-30
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