Jobs at Cerebras Systems Inc.

Bring up and validate next-generation AI hardware systems and supporting software. Debug hardware-software integration failures using logs, telemetry, and diagnostic tools; develop automation frameworks, testing infrastructure, and observability tooling; reproduce and triage complex issues; and collaborate with hardware engineers to qualify systems for production.

Designs and operates secure, scalable cloud infrastructure and identity platforms across AWS and data centers. Builds IAM, authentication, authorization, SSO, MFA, provisioning, secrets, and key management solutions. Develops Terraform-based automation using Python or Go, supports Kubernetes services, implements Zero Trust controls, and improves reliability, observability, and developer experience. The role includes architecture reviews, cross-team technical leadership, mentoring, production support, incident response, and on-call participation.

Designs and leads CI/CD, artifact lifecycle, build, test, release, and developer infrastructure systems. Improves cloud infrastructure, code review workflows, branching strategies, automation, observability, and engineering productivity. Troubleshoots complex distributed systems, drives architectural improvements, participates in on-call and incident response, and mentors engineers across the Developer Productivity organization. Contributes to AI tooling that automates repetitive engineering workflows.

Design and deliver production FPGA solutions for chassis-to-wafer IO including RoCE v2/RDMA network interfaces, switching fabric, and serial IO. Develop RTL, produce bitstreams, optimize bandwidth and latency, debug large AI cluster networking, coordinate board bringup, and lead cross-functional projects with DV, embedded software, and architecture teams.

Operate, monitor, and optimize large-scale AI compute clusters built on the Wafer-Scale Engine. Develop and own software for cluster operations (monitoring, automation, APIs, dashboards), deploy and debug Docker-based services, troubleshoot distributed systems, maximize compute capacity, and participate in a 24/7 on-call rotation while collaborating cross-functionally to improve operational visibility and reliability.

Build and maintain the inference orchestration layer across datacenter clusters. Design and implement highly available, low-latency systems, define platform direction (Kubernetes CRDs/operators), lead production incident response, optimize performance and capacity, drive observability and security, and collaborate with ML, product, and cloud teams to productionize scalable inference services.

Design, build, and operate CI/CD, Kubernetes-based platforms, deployment automation, and observability for engineering workflows. Improve reliability, performance, and scalability across cloud and on-prem environments, debug cross-boundary failures, perform root-cause analysis, and deliver durable platform software and self-service tooling.

Lead testing strategy and execution for ML API features, building scalable test frameworks and automation to validate accuracy, fairness, and performance of inference systems. Drive pre-deployment and production validation across distributed cloud, multi-region, and hardware-backed inference environments. Mentor SDETs, champion best practices, debug complex distributed issues, and lead cross-functional quality initiatives to improve test coverage and engineering efficiency.

New graduate software engineer working on low-level, performance-oriented software for AI accelerator systems. Design, implement, test, and debug hardware-near components, support system bring-up, performance optimization, tooling, and cross-functional collaboration with hardware, firmware, compiler, and infrastructure teams.

Bring up state-of-the-art open-source or proprietary LLMs on Cerebras CSX systems across the full stack: model translation, graph lowering, compiler optimizations, runtime integration, performance tuning, debugging, and prototyping tooling improvements to accelerate future bring-ups.

Build and maintain CI/CD pipelines and artifact lifecycle systems, provision and optimize cloud (AWS) CI infrastructure, troubleshoot build and pipeline issues, improve developer tooling and build/test infrastructure, support code review/workflow improvements, participate in on-call incident response, and contribute to AI tooling to boost engineering productivity.

Design, build, and operate secure, scalable cloud infrastructure and identity platforms across AWS and data centers. Implement IAM/IGA, SSO/MFA, federation protocols, secrets and key management, and Zero Trust controls. Automate with Terraform, Python, and Go, deploy on Kubernetes, participate in on-call and incident response, and collaborate with Security and Engineering to deliver secure-by-design systems.

Build and improve C++ simulator infrastructure for Cerebras Wafer-Scale Engine, covering functional and pipeline-accurate simulation. Develop tooling, tests, and performance improvements; collaborate with architecture, design verification, compiler/runtime teams; debug cross-domain issues and maintain test/regression coverage and documentation.

Lead design and execution of automated tests for large-scale AI clusters. Define test strategies, automate reliability, performance, stress, failure, and security tests across software (Kubernetes, monitoring) and hardware (wafer-scale accelerators, CPUs, interconnects). Debug distributed systems and datacenter hardware, and champion cluster uptime and observability.

Build and productionize a GPU-accelerated prefill serving path for LLM inference: deploy, operate, and optimize model-serving, runtime (vLLM/PyTorch/ROCm), multi-node GPU infrastructure, reliability, performance, correctness, and benchmarking.

Develop and maintain infrastructure to build, test, operate, simulate, and evaluate Cerebras software. Design scalable automation workflows for cloud and datacenter, manage CI/CD, containerized deployments, and monitor software quality and performance on the Wafer Scale Engine.

Lead capacity planning and fleet strategy for the Inference Service: build rolling forecasts, support datacenter bring-up, decide model/cluster allocation, run utilization reporting, drive capacity tool adoption, coordinate cross-functional execution, and manage incident postmortems.

Lead automation and platform engineering to eliminate toil and deliver self-service GitOps-driven CD, capacity provisioning, and observability for large-scale inference clusters. Define SLOs/SLIs, mentor SREs, support incident escalation, implement reliability practices, and measure impact via deployment velocity, SLO compliance, MTTR, and adoption of self-service workflows.

Characterize and optimize performance of state-of-the-art AI models on Cerebras hardware. Build kernel-level and end-to-end performance models, debug kernel microcode and compiler algorithms, analyze runtime and cluster performance, and develop tools to visualize performance data for the Wafer Scale Engine and compute cluster.

Lead design, development, and operation of a globally distributed inference orchestration platform. Drive architecture, reliability, performance, observability, security, and CI/CD. Own critical production systems, mentor senior engineers, resolve high-impact incidents, and collaborate with ML, Product, Infrastructure, and Cloud teams to deliver scalable, low-latency inference services.