Jobs at Cerebras Systems Inc.

Design and optimize hyperscale data pipelines and ETL for multi-terabyte hardware telemetry. Build ML-driven failure prediction and anomaly detection, create dashboards and real-time telemetry visualizations, lead hardware characterization and A/B studies, perform root cause analysis, and collaborate with cross-functional teams to improve hardware performance, reliability, and energy efficiency for AI platforms.

Perform post-silicon validation and debug of wafer-scale engines; characterize high-speed serial interfaces; support wafer bring-up, burn-in, and manufacturing operations; develop hardware, automated regression tests, and Python/Bash debug tools; collaborate with hardware and system software teams and document issues and flows.

Lead product strategy and execution for strategic AI customers: embed with lighthouse accounts, design PoCs showcasing ultra-low-latency inference, advise on model selection and long-term AI strategy, drive adoption and expansion, and translate customer insights into roadmap priorities across product, engineering, and GTM teams.

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.

Develop and implement verification strategies and reusable testbenches (SystemVerilog/UVM/DPI). Create tests, run regressions, collect coverage, debug across simulation, emulation, and silicon bring-up, and collaborate with architecture, RTL, firmware, and validation teams to improve verification infrastructure and methodology.

Own and architect the Inference Cloud Platform: design multi-region topology, service discovery, routing, load balancing, caching, batching, and traffic management. Improve availability, latency, throughput, and resilience at high QPS. Lead production incident response, observability, capacity planning, and mentor senior engineers while making high-consequence architectural decisions and delivering production code.

Build and maintain automation for bare-metal cluster deployments: provisioning, configuration, validation, and operational handoff. Convert manual steps into tested pushbutton workflows, troubleshoot Linux, networking, storage, and Kubernetes issues, and add observability and health checks to improve deployment reliability.

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.

Design, implement, and maintain automated assembly and test systems. Program and troubleshoot PLCs, HMIs, SCADA, robotics, and industrial controls. Improve productivity and quality, lead automation projects, perform root-cause analysis, create control documentation, train staff, and support preventive maintenance and safety compliance.

Design and evaluate micro-architecture features for an AI accelerator. Build and validate performance and power models, perform PPA trade-offs, profile workloads, identify bottlenecks and HW acceleration opportunities, and collaborate with software teams for cluster-level application modeling and benchmarking.

Lead end-to-end bring-up of ML frameworks and models on Cerebras CSX systems, spanning model translation, compiler lowering, runtime integration, debugging performance/numerical issues, and prototyping tooling or API improvements to accelerate future deployments.

Design, deploy, and maintain Linux servers and high-speed LAN/WAN networking across manufacturing sites. Implement automation (Ansible, Terraform), manage Palo Alto security, core switching (Arista/Juniper/Cisco), Wi‑Fi and ruggedized hardware, ensure redundancy/QoS, monitor performance, provide Tier 2/3 support and on‑call response, and lead IT/OT modernization and documentation efforts.

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.

Design, deploy, and maintain global WAN connectivity across leased lines and dark fiber. Collaborate with carriers, configure and troubleshoot routing and security (BGP, IPSec, MACsec, VXLAN/EVPN), implement redundancy/QoS, monitor performance, support cloud connectivity (AWS/Azure/GCP), provide Tier 3 incident response, and automate network configuration with Python, Ansible, or Terraform.

Build and run operational analytics, KPI architectures, dashboards, and closed-loop processes across supply chain, manufacturing, and data center deployment. Partner with operations leaders to diagnose bottlenecks, redesign processes, automate reporting, run operating cadence, and deliver executive-ready insights that drive measurable operational improvements.

Entry-level engineer working at the software/hardware interface: kernel development, ASIC performance modeling, system bringup, performance profiling and debugging. Learn Cerebras Wafer Scale Engine end-to-end, model and tune performance, and validate designs on hardware and simulators using C++ and Python.

Adapt and optimize transformer-based language and vision models to run on Cerebras hardware. Design, prototype, and validate inference techniques (speculative decoding, pruning, sparse attention). Run experiments, profile performance, debug models, and collaborate with ML, software, and hardware teams to deploy low-latency, high-throughput inference solutions.

Lead architecture and roadmap for an end-to-end manufacturing test software platform spanning cloud and on-site client-server systems. Manage and mentor a full-stack engineering team, make cross-stack architectural decisions, drive integrations with hardware, data, and operations, and improve manufacturing efficiency through tooling, automation, and data infrastructure.

Drive end-to-end ML inference performance by building kernel- and system-level performance models, optimizing kernel microcode and compiler algorithms, debugging runtime and cluster performance, and developing tools to visualize performance data from the Wafer Scale Engine and compute cluster.