Sunnyvale, CA
Wafer-scale AI processors and supercomputers
Cerebras builds wafer-scale AI chips like the Wafer Scale Engine and systems such as CS-3 for training and inference of large AI models. They serve AI companies, enterprises, research labs, and governments needing fast, scalable compute. It enables breakthrough performance in AI applications like real-time inference and drug discovery.
Operates and develops software for large-scale AI compute clusters, improving reliability, capacity, monitoring, and incident response. Requires 6–8 years of complex infrastructure experience, strong Python and Go skills, distributed-systems expertise, and participation in 24/7 on-call support.
Leads design verification for Cerebras AI chip architecture, developing scalable SystemVerilog/UVM environments, tests, assertions, and coverage plans while debugging across simulation, emulation, and silicon bring-up. Requires 10+ years of verification experience and strong programming and hardware-debugging skills.
Leads post-silicon bring-up and optimization of Cerebras Wafer Scale Engines, developing validation infrastructure, diagnostics, testing automation, and performance improvements across hardware and software. Requires 7–10+ years of industry experience and strong ASIC, coding, debugging, and cross-functional collaboration skills.
Design and lead FPGA-based host and network IO systems spanning chassis-to-wafer communication, RoCE networking, switching fabrics, and proprietary serial IO. The role requires production FPGA experience, Verilog expertise, timing closure, and network protocol/debugging knowledge.
Develop and review electrical infrastructure designs for high-density AI data center clusters, translating system power requirements into safe, scalable facility architectures. Requires 7+ years experience with mission-critical facilities, power distribution equipment, and electrical studies.
Lead the technical development of data center infrastructure standards and deployment designs for high-density AI systems, owning the Basis of Design for power, cooling, mechanical and electrical infrastructure while partnering with internal engineering teams and external colocation providers.
Debug and root-cause complex electrical, thermal, and mechanical failures in Cerebras' massive AI compute systems both in-lab and in the field. Drive learnings back into next-generation hardware design while remotely diagnosing deployed systems using Linux tools.
Build and optimize Cerebras’s production GPU inference stack across APIs, vLLM, PyTorch, ROCm, distributed systems, and AMD infrastructure. The role requires 8+ years of software engineering experience, strong C++ and Python skills, and deep expertise in GPU performance, reliability, and model serving.
Design and optimize hyperscale data pipelines and ML models for multi-terabyte hardware telemetry, performance analysis, anomaly detection, reliability forecasting, and efficiency optimization of AI servers and silicon hardware. Requires Master's degree and 3+ years experience.
Senior QA Engineer designing automated test frameworks and executing functional, integration, regression, and performance tests for Cerebras' cloud-based AI inference services and distributed ML systems using Python, Java, Selenium, and CI/CD tools. Requires Master's in CS/CE and 4+ years QA experience.
The Junior SDET will build automated tests, diagnostics, and qualification workflows for AI inference systems, while triaging cross-stack integration and release failures. The role suits an early-career engineer with programming fundamentals, hands-on software experience, and interest in distributed systems, AI infrastructure, or hardware acceleration.
Leads Release Integration Testing for an AI inference platform, establishing readiness gates, cross-stack validation, release regression, rollout triage, and quality metrics. The role requires strong software engineering, test architecture, distributed-systems validation, and technical leadership across software, infrastructure, and hardware teams.
Build and operate the platform layer behind Cerebras engineering infrastructure, including CI/CD, Kubernetes, deployment automation, cloud and on-premises systems, developer environments, and observability. The role requires 5+ years of infrastructure or software engineering experience and strong debugging and systems fundamentals.
Build Python frameworks, orchestration systems, scheduling software, test infrastructure, and developer tools that coordinate complex engineering workflows across machines and clusters. The role requires at least three years of software-engineering experience, strong Python and systems fundamentals, and expertise in concurrency, debugging, and distributed-systems concepts.
Senior Digital Infrastructure Counsel supporting Cerebras' Global Data Centers team by owning legal strategy, execution, and relationships for data center acquisitions, infrastructure transactions, and related deals. Requires 7+ years experience in infrastructure transactions at data center providers or hyperscalers, JD, and deep knowledge of digital infrastructure, real estate, and technical terms.
Develops and optimizes low-level machine learning and linear algebra kernels for Cerebras's Wafer-Scale Engine. The role is suited to a new graduate with strong C++ fundamentals and interest in computer architecture, parallel programming, performance optimization, and hardware/software co-design.
Leads a globally distributed engineering organization enabling foundation models and generative AI workloads on Cerebras hardware. The role requires deep compiler or ML infrastructure expertise, substantial engineering leadership experience, and cross-functional collaboration across hardware, runtime, cloud, and research teams.
Build and maintain automation tooling for large-scale AI compute cluster deployments, turning bare-metal infrastructure into repeatable, pushbutton workflows using Python, Ansible, Terraform, Kubernetes and observability tools. Ideal for new graduates or early-career engineers seeking hands-on production infrastructure experience.
Lead the development and execution of global IP strategy for AI hardware and software, including patent portfolio building, invention harvesting, and advising on IP risks and transactions. Requires JD, USPTO registration, 10+ years patent prosecution experience, and technical background in AI/semiconductors.
Lead end-to-end deployment of large-scale AI clusters in data centers, from facility readiness to customer handoff. Manage rack integration, high-density cabling, facilities coordination, troubleshooting, and cross-functional execution for hyperscale AI infrastructure.
Lead global capacity planning and delivery for AI infrastructure at Cerebras. Own end-to-end lifecycle from demand forecasting through deployment, build TPM team, and drive metrics for predictability and capital efficiency. Requires 7-10 years TPM/infrastructure experience and 5+ years leading teams.
Full-stack engineer building and scaling the Cerebras Developer Console. Design and implement frontend (Next.js, TypeScript), backend services (GraphQL, Postgres, Redis) for billing, quotas, observability, and metrics at high scale.
Infrastructure Engineer responsible for hands-on installation, provisioning, maintenance, and troubleshooting of high-performance on-premise server hardware, Linux systems, and high-speed networking (100G/400G) in a data center environment. Requires 3+ years experience with Linux admin, x86 hardware, and network configuration.
Support the Head of Data Center Acquisition by sourcing and triaging data center capacity opportunities through broker outreach, market intelligence, early diligence, and pipeline management. Requires 1-4 years in acquisitions, real estate, infrastructure or sourcing with strong analytical and outreach skills.
New college grad software engineer building and improving functional and pipeline-accurate simulators in C++ for Cerebras' wafer-scale AI engine. Requires strong C++ and computer architecture knowledge to support hardware/software co-development across kernel, compiler, and verification teams.
Characterize, analyze, and optimize performance of state-of-the-art AI models on Cerebras' wafer-scale hardware. Build performance models, optimize kernels and compilers, debug runtime behavior, and develop visualization tools to influence next-gen AI architecture.
Build and debug software infrastructure for next-generation AI hardware systems at Cerebras. Requires strong Python/C++ skills, OS and computer architecture knowledge, and experience with complex hardware-software debugging and system integration.
Senior ERP Systems Administrator responsible for managing, supporting, and optimizing NetSuite and adjacent enterprise platforms. Requires 6+ years ERP admin experience including 4+ years with NetSuite, strong Finance and Supply Chain knowledge, and SOX compliance background.
Build and maintain test infrastructure and automation to validate the Cerebras Inference Platform across cloud Kubernetes and hardware clusters. Ensure reliability of deployment stack including CI/CD, ingress, load balancing, and observability for massive-scale AI inference.
Build and maintain CI/CD pipelines, artifact management, cloud infrastructure, and developer productivity tooling at Cerebras to accelerate AI hardware and software engineering. Requires 2-5 years DevOps/infrastructure experience, Kubernetes, AWS, and strong troubleshooting skills.
Design, build, and operate secure cloud infrastructure and identity platforms in AWS and on-prem data centers. Implement IAM, automation with Terraform/Python/Go, security controls for AI systems, and Zero Trust principles while participating in on-call.
Serve as owner's technical representative providing mechanical infrastructure oversight for high-density AI data center deployments. Review designs, manage colocation partners, support construction/commissioning, and ensure operational readiness of cooling systems (8+ years mission-critical/hyperscale data center experience required).
Principal SRE to architect self-service reliability platforms, capacity orchestration, and production control planes for Cerebras' ultra-high-speed AI inference infrastructure at massive scale. Requires 15+ years in SRE/platform engineering with deep large-scale fleet and observability experience.
Design, implement, and optimize automated manufacturing assembly and test systems using PLCs, robotics, and industrial controls. Requires 3+ years in manufacturing automation, strong troubleshooting skills, and a bachelor's in engineering.
Senior Systems Engineering Technician responsible for building, testing, and troubleshooting complex mechanical, electrical, and water-cooled hardware systems in a prototype lab. Requires 5+ years hands-on multidisciplinary experience, independent work with incomplete documentation, and strong mechanical/electrical diagnostic skills.
Technical Program Manager leading capacity planning, forecasting, allocation, and fleet strategy for Cerebras' high-speed AI inference service. Own rolling capacity models, datacenter bring-up, weekly reviews with SRE/Product/Eng, tool adoption, and strategic initiatives to maximize utilization of the world's largest AI chips.
Lead operations, deployment, and performance of high-density AI data centers in Finland and broader Europe. Requires 5-10+ years data center experience, MEP systems knowledge, team leadership, and bilingual Finnish/English fluency.
Senior SDET leading testing strategy, automation, and quality validation for Cerebras ML Inference API features, including accuracy, fairness, and performance at scale for AI models and hardware-backed services. Requires 5+ years experience building test frameworks and debugging large-scale distributed systems.
Leads hands-on security engineering for AI platforms, enterprise infrastructure, model-serving systems, customer workloads, and agentic automation. The role requires 10+ years of technical security experience, strong programming and cloud security skills, and expertise in identity, runtime protection, authorization, and AI security.
Leads the architecture and evolution of secure, resilient enterprise, data center, and cloud networks supporting high-performance AI infrastructure. Requires 10+ years of large-scale network engineering experience, deep routing and fabric expertise, cloud networking knowledge, and strong automation skills.
Designs and operates firewall, segmentation, zero-trust, and network detection controls across data center, corporate, and AWS environments. The role requires deep network security expertise, infrastructure-as-code automation with Terraform or Ansible, scripting proficiency, and strong TCP/IP troubleshooting skills.
Hardens Cerebras compute platforms across the Linux kernel, firmware, secure boot, and hardware roots of trust. The role requires deep systems security expertise, strong C and systems-language skills, and experience with low-level isolation, monitoring, and attestation.
Software engineer building and operating the orchestration layer for a globally distributed, high-performance AI inference platform on custom wafer-scale hardware.
Hands-on technical lead building and operating the orchestration layer for a globally distributed, high-performance AI inference platform on custom wafer-scale hardware.
Design and analyze 3D integrated ASIC/SoC products, performing physical verification, power/clock/cooling analysis, and collaborating with RTL teams on novel 3D packaging concepts. Requires 10+ years of physical design experience and expertise with ICV/Calibre, IR/EM, and scripting.
Founding PM for Strategic Verticals at Cerebras. Embed with top AI customers to co-architect solutions, drive adoption, and shape the product roadmap for wafer-scale AI inference.
Design and develop next-generation Wafer Scale Engine chips with deep RTL expertise, focusing on high-performance, power-efficient micro-architecture and integration. Requires 8+ years RTL experience and a Master's degree.
Develop verification strategies, reusable testbenches, and coverage plans for large-scale AI chip designs. Requires 3+ years of Design Verification experience with SystemVerilog, UVM, and strong debugging skills.
Lead design verification for wafer-scale AI chips. Develop verification strategies, reusable testbenches, and coverage plans while collaborating with architecture and design teams. Requires 10+ years of experience and deep expertise in SystemVerilog and UVM.
Architect new AI chip features by translating specs into micro-architecture requirements, building performance/power models, and conducting PPA trade-offs. Requires 10+ years in performance modeling for GPUs/CPUs/accelerators and a Master's or PhD.