Distributed Software Engineer
Read straight from Cerebras Systems's careers page, verified 3d ago.Seekless can tailor your résumé to it.
Seekless can write your résumé for this posting first.
Before you apply
LocationToronto, CAN
Work modehybrid
Typefull-time
SenioritySenior
Experience5+ yrs
DepartmentSoftware Engineering
Company size501–1,000 people
PostedSep 8, 2026 · 1mo ago
Verified live3d ago
At a glanceSummarised by Seekless from the posting.
5+ years building and operating production distributed systems or infrastructure software
Production-quality Go and Python
Real Kubernetes depth: written or debugged controllers and operators, understanding 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
Demonstrated adoption of AI in engineering workflow: active use of coding agents
Nice to have: bare-metal or HPC fleet operations
Nice to have: scheduler internals
Nice to have: RDMA/RoCE and eBPF networking
Nice to have: Ceph or NVMe-oF
Nice to have: etcd and HA upgrades
Nice to have: inference serving stacks
Skills
Go
Python
Kubernetes
Prometheus
Grafana
gRPC
Redfish
IPMI
gNMI
sFlow
Ceph
NVMe-oF
etcd
RDMA
RoCE
eBPF
About the company
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; 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 works with the leading 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.
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.
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
Skills and 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, and 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.
Nice to have
· 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.
Why Join Cerebras
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. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:
1.
Build a breakthrough AI platform beyond the constraints of the GPU.
2.
Publish and open source their cutting-edge AI research.
3.
Work on one of the fastest AI supercomputers in the world.
4.
Enjoy job stability with startup vitality.
5.
Our simple, non-corporate work culture that respects individual beliefs.
Find out more about what it’s like to work at Cerebras here!
Apply today and become part of the forefront of groundbreaking advancements in AI!
Legal
Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. 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.
This website or its third-party tools process personal data. For more details, click here to review our CCPA disclosure notice.