Cerebras Systems

Title: Design Verification Engineer Location: Sunnyvale, CA Job Description: Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top 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. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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. Key Responsibilities - Work with architects, designers, post-silicon, and software engineers, to ensure a high-quality design that works for silicon. - Develop and implement verification strategies, detailed tests, and coverage plans based on micro-architecture. - Create verification methodologies and reusable environments, including components such as stimulus, checkers, assertions, and coverage. - Implement tests, manage regressions, gather coverage, and debug test failures. - Collaborate with cross-functional teams, including architecture, RTL design, physical design, firmware, and validation. - Analyze and debug complex issues across simulation, emulation, and silicon bring-up phases. - Continuously enhances verification infrastructure and flows to improve efficiency and quality. - Contribute to the evolution of the overall verification methodology and best practices across the organization. Skills and Qualifications - Advanced debugging and problem-solving skills. - Deep knowledge of SystemVerilog testbench, DPI, and UVM. - Excellent programming skills and knowledge of software engineering practices, including object-oriented design. - Experience developing scalable and portable testbenches and components. - Experience with verification methodologies and tools such as simulators, waveform viewers, build and run automation, coverage collection, and gate-level simulations. - Proficient in scripting languages such as Python or Perl. - Good interpersonal skills and the ability to work as a standout colleague are a must. - Extremely self-motivated and eager to solve problems - 3+ years of Design Verification experience. Desired Skills and Qualifications - Knowledge of pipelined processor architecture. - BS or MS in Computer Science or Electrical Engineering. - 3+ years of hands-on Design Verification experience. The base salary range for this position is $120,000 to $240,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications. We are open to remote candidates. 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: - Build a breakthrough AI platform beyond the constraints of the GPU. - Publish and open source their cutting-edge AI research. - Work on one of the fastest AI supercomputers in the world. - Enjoy job stability with startup vitality. - Our simple, non-corporate work culture that respects individual beliefs. Read our blog: Five Reasons to Join Cerebras in 2026. Apply today and become part of the forefront of groundbreaking advancements in AI! 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.

Role Description Cerebras Systems Inc. has multiple openings for Member of Technical Staff (Software Engineer). - Implement infrastructure to support high-performance, low-latency inference service. - Deploy and configure Kubernetes services to ensure scalability and reliability of inference workloads. - Optimize resource allocation and auto-scaling policies to handle variable inference demand while minimizing operational costs. - Integrate inference services with containerized environments using Docker and Kubernetes for orchestration. - Ensure high availability and fault tolerance by implementing multi-region deployments and disaster recovery strategies. - Develop Python-based scripts and APIs to streamline data preprocessing, inference execution, and post-processing for real-time inference tasks. - Collaborate with machine learning engineers to validate inference accuracy and performance against functional and latency requirements. - Triage and resolve defects in the service by analyzing logs, metrics, and distributed traces. - Debug issues related to model deployment, container orchestration, or networking configurations, documenting steps to reproduce and root-cause defects. - Collaborate with cross-functional teams to address performance regressions, scalability issues, or integration failures in the inference pipeline. - Develop automated scripts to detect and mitigate common failure modes, improving system reliability. - Author detailed technical documentation for infrastructure configurations, inference workflows, and APIs, ensuring clarity for internal teams and external customers. - Work with product management and user experience teams to define requirements for inference service interfaces, including configuration, monitoring, and event logging. - Document and track defects, enhancements, and release notes using tools like Jira and Git, ensuring version control and traceability. - Participate in release planning and prioritization discussions to align infrastructure development with customer needs and business objectives. Qualifications - Master’s degree or foreign equivalent degree in Computer Science, or a related field. - 1 year of experience as Software Developer, Student/Intern (Software Developer), Member of Technical Staff (Software Engineer), Software Engineer, or a related occupation. - Employer accepts full-time or equivalent part-time experience gained before, during or after graduate studies. Requirements - Docker and Kubernetes - Java or C++ - ActiveMQ and Kafka - Python or Groovy - JavaScript or TypeScript - Linux - SQL, OracleDB, and Redis - Git Benefits - Build a breakthrough AI platform beyond the constraints of the GPU. - Publish and open source their cutting-edge AI research. - Work on one of the fastest AI supercomputers in the world. - Enjoy job stability with startup vitality. - Our simple, non-corporate work culture that respects individual beliefs. Company Description Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. Cerebras' current customers include top 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. Thanks to the groundbreaking wafer-scale architecture, Cerebras Inference offers the fastest Generative AI inference solution in the world, 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.

Role Description Cerebras Systems Inc. has multiple openings for Sr. Technical Staff. - Post silicon validation of Cerebras Wafer Scale Engines. Test and debug issues on new silicon. - Test, analyze, and characterize high-speed serial interfaces to verify compliance with hardware specifications, record performance data, and recommend design modifications to optimize functionality. - Work with the silicon and operations team to test, bring-up and run burn-in on wafer scale systems. - Support manufacturing operations to utilize the wafer bring up flow. Perform wafer bring-ups, diagnose and debug problems encountered. - Develop and implement hardware to ensure compliance with design specifications. - Collaborate with hardware design engineers and system software engineers to review specifications and to recommend changes that will improve the quality and verifiability of the hardware designs. - Create and maintain automated regression test scripts, using Python and/or bash, that ensure that all tests are run and pass after each change to the design, testbench, tests, or reference model. - Work with system team members to diagnose system related failures. Understand the key system interfaces to FPGA’s, power and cooling, and apply that knowledge to the debug of silicon features. - Development of debug tools in Python to program and analyze the behavior of the Wafer Scale Engine. - Development of wafer bring up flow utilizing Python and shell scripts to capture the steps required to bring up a wafer in a logical easy to use flow. - Documentation of issues found, tools and flow. Qualifications - Master’s degree or foreign equivalent degree in Electrical Engineering, Computer Engineering, or a related field. - 3 years of experience as Application Engineer, Sr. Technical Staff, Hardware Engineer, or a related occupation. Requirements - Electrical Signal Integrity Analysis. - Hardware Bring-up & Debug. - Functional and Electrical characterization. - Test automation using scripting language. - High Speed Interfaces & Protocols including Ethernet, CPRI, or Interlaken. Benefits - Salary Range: $250,000.00 per year to $275,000.00 per year. - Telecommuting permitted. Company Description Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. This approach allows Cerebras to deliver industry-leading training and inference speeds and empowers machine learning users to effortlessly run large-scale ML applications, without the hassle of managing hundreds of GPUs or TPUs. - Current customers include top 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. - Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

Role Description Cerebras Systems Inc. has multiple openings for Sr. Member of Technical Staff. - Design and develop software features that support system resiliency and high availability, including automated recovery mechanisms and fault-tolerant architecture across distributed environments. - Develop and maintain cloud-based deployment workflows for AI inference software using AWS tools and services to support low-latency and scalable system performance. - Develop Python-based scripts and APIs to streamline data preprocessing, inference execution, and post-processing for real-time inference tasks. - Use parallel programming techniques (e.g., multi-threading, asynchronous processing) to maximize resource efficiency on AWS compute instances. - Develop software components to support visualization and analysis of system performance metrics, enhancing the monitoring and usability of inference services. - Develop inference software in Docker containers and define Kubernetes orchestration strategies that ensure software reliability and efficient scaling. - Develop automated scripts to detect and mitigate common failure modes, improving software system reliability. - Debug issues related to model deployment, container orchestration, networking configurations, documenting steps to reproduce and root-cause defects. - Triage and resolve defects in the software service by analyzing logs, metrics, and distributed traces using tools like AWS CloudWatch, Grafana, or custom Python scripts. - Work with product management and user experience teams to define requirements for inference service interfaces, including configuration, monitoring, and event logging. - Author detailed technical documentation for infrastructure configurations, inference workflows, and APIs, ensuring clarity for internal teams and external customers. - Document and track defects, enhancements, and release notes using tools like Jira and Git, ensuring version control and traceability. Qualifications - Master’s degree or foreign equivalent degree in Computer Science, or a related field. - 18 months of experience as Information Security Analyst, Software Engineer, Sr. Member of Technical Staff, IT Senior Applications Engineer, or a related occupation. Requirements - Infrastructure-as-Code and deployment automation: Terraform, AWS CloudFormation, AWS CDK, and Ansible. - Containerization and orchestration: Docker, Kubernetes, AWS EKS, AWS Elastic Container Service (ECS), AWS Fargate, and Helm. - Compute and serverless services: AWS EC2, AWS Lambda functions, and Auto Scaling Groups. - Monitoring, logging, and distributed tracing: AWS CloudWatch, AWS X-Ray, ELK (Elasticsearch, Logstash, Kibana), Prometheus, and Grafana. - Programming languages and frameworks: Python, Node.js, JavaScript, and Flask. - Data storage and caching: PostgreSQL, Redis, and NFS. - CI/CD and version control: Jenkins and Git. Benefits - Salary Range: $230,000.00 per year to $250,000.00 per year. - Telecommuting permitted. Company Description Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. Our novel wafer-scale architecture provides the AI compute power of dozens of GPUs on a single chip, with the programming simplicity of a single device. - Current customers include top 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. - Cerebras Inference offers the fastest Generative AI inference solution in the world, over 10 times faster than GPU-based hyperscale cloud inference services.

• Drive end-to-end Data Center Delivery execution across data center builds • Own schedule, scope, and cross-functional alignment • Ensure seamless execution from design to commissioning • Build and manage integrated EPC schedules • Drive critical path execution and recovery plans • Lead weekly execution reviews with contractors and internal teams • Align engineering, supply chain, construction, and commissioning • Eliminate gaps between design intent and field execution • Resolve technical interfaces (power, cooling, controls, network) • Track long-lead equipment • Ensure procurement stays ahead of construction • Mitigate supply chain risks early • Work directly with general contractors and trades • Resolve RFIs and field issues • Ensure execution matches design and schedule • Coordinate commissioning across systems • Drive readiness for Ready for Service (RFS) • Ensure sites are operational • Identify risks early and drive mitigation • Maintain visibility into issues and blockers • Provide concise updates to leadership • Escalate with solutions and trade-offs.

Role Description Cerebras is seeking a Commissioning Lead to own the end-to-end commissioning and readiness of AI data center infrastructure across colocation environments. This role is responsible for ensuring all systems are tested, validated, and fully operational prior to handover, with zero tolerance for failures in mission-critical environments. You will operate with high ownership in a fast-paced startup environment, driving commissioning execution across multiple concurrent sites and ensuring rapid, reliable capacity bring-up. Responsibilities - Lead commissioning strategy and execution across all colo data center deployments. - Own full lifecycle commissioning from Level 1–5 testing through integrated systems testing (IST). - Develop and enforce commissioning plans, scripts, and procedures. - Coordinate with construction, engineering, vendors, and colo providers to ensure readiness. - Oversee testing of electrical systems (switchgear, UPS, generators), mechanical systems (cooling), and IT infrastructure. - Ensure all systems meet design intent, performance requirements, and reliability standards. - Drive issue identification, resolution, and closure prior to handover. - Manage commissioning agents, vendors, and third-party testing teams. - Establish standardized commissioning processes for repeatable deployments. - Track and report commissioning progress, risks, and readiness to executive leadership. - Ensure all documentation, test results, and turnover packages are complete and accurate. - Validate base building readiness from colo providers prior to fit-out energization. - Coordinate integration between landlord systems and tenant infrastructure. - Ensure alignment on power availability, redundancy, and cooling capacity. - Resolve interface issues between colo infrastructure and Cerebras systems. - Hold providers accountable for performance during testing and energization. Qualifications - 10–15+ years of experience in commissioning of mission-critical facilities. - Deep expertise in data center electrical and mechanical systems. - Experience leading Level 1–5 commissioning for large-scale projects. - Strong understanding of high-density compute environments. - Experience working in colo environments and coordinating landlord/tenant interfaces. - Proven ability to manage multiple sites and fast-track deployments. - Strong troubleshooting and problem-solving skills. - Ability to operate in a fast-paced, high-growth startup environment. - Excellent communication and stakeholder management skills. Location Remote, USA Salary The base salary range for this position is $220,000 to $260,000 annually. Actual compensation may include bonus and equity, and will be determined based on factors such as experience, skills, and qualifications. Benefits - Build a breakthrough AI platform beyond the constraints of the GPU. - Publish and open source their cutting-edge AI research. - Work on one of the fastest AI supercomputers in the world. - Enjoy job stability with startup vitality. - Our simple, non-corporate work culture that respects individual beliefs. Company Description 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.

• Own end-to-end fiber and network infrastructure deployment across colo data center sites. • Design fiber pathways, structured cabling systems, and high-density fiber distribution architectures. • Oversee installation of fiber (SMF/MMF), patch panels, trays, and cable management systems. • Coordinate with construction and commissioning teams to align network readiness with overall site delivery. • Validate fiber installations including testing (OTDR, insertion loss, continuity). • Support deployment of network hardware including switches, routers, and interconnects. • Ensure low-latency, high-bandwidth connectivity across racks and clusters. • Develop and maintain standards for fiber design, labeling, and documentation. • Troubleshoot network and fiber issues during deployment and post-handover. • Manage vendors, installers, and low-voltage contractors. • Track progress, risks, and readiness across multiple sites. • Coordinate with colo providers for meet-me room (MMR) connectivity and cross-connects. • Ensure alignment on demarcation points and handoff standards. • Manage external connectivity including ISP, dark fiber, and backbone integration. • Validate provider fiber infrastructure and resolve interface issues.

• Own procurement strategy for all data center equipment including electrical, mechanical, and IT infrastructure • Lead sourcing and contracting for major equipment packages (switchgear, transformers, UPS, generators, cooling systems, racks) • Negotiate commercial terms, pricing, lead times, and risk allocation with suppliers and contractors • Develop and manage master service agreements (MSAs), purchase agreements, and vendor contracts • Drive cost optimization while maintaining speed and quality of delivery • Partner with construction and engineering teams to align procurement with deployment schedules • Manage supplier performance, ensuring on-time delivery and quality compliance • Identify and mitigate supply chain risks, including long-lead constraints and market volatility • Build strategic relationships with key OEMs and vendors • Establish scalable procurement processes, tools, and reporting mechanisms • Track and forecast spend across multiple projects and regions • Lead contract negotiations for EPCs, GCs, and major vendors • Define contract structures that align incentives with schedule and performance outcomes • Manage change orders, claims, and commercial disputes • Ensure clear scope definition and risk allocation across all agreements • Standardize contract templates for rapid deployment across multiple sites • Partner with legal and finance to ensure compliance and governance.

• Own end-to-end delivery of data center sites. • Lead all aspects of data hall buildout including racks, containment, power distribution, cooling integration, and network readiness. • Act as the primary interface with colo providers to align on base build readiness, SLAs, and integration points. • Drive Vendors, GCs, and trade partners to execute fit-out scope on aggressive timelines. • Establish construction standards, playbooks, and repeatable deployment models for colo-based builds. • Ensure seamless coordination between base building and tenant improvement scopes. • Manage cost, schedule, and quality across a portfolio of concurrent deployments. • Identify risks early and implement mitigation strategies to avoid delays to capacity delivery. • Lead site-level execution teams and ensure strong on-the-ground presence where required. • Partner with procurement and supply chain to ensure timely delivery of long-lead equipment. • Implement real-time reporting and dashboards for executive visibility. • Drive innovation in deployment speed including modularization and prefabrication approaches. • Establish and manage strategic relationships with colocation providers. • Define and enforce technical and delivery requirements for base building readiness. • Ensure clear demarcation of responsibilities between landlord and tenant scopes. • Hold providers accountable to schedule commitments, power delivery milestones, and facility readiness. • Resolve integration issues between colo infrastructure and Cerebras systems. • Negotiate scope, timelines, and change orders with providers and contractors.

• Analysis of new models from generative AI field and understanding of impacts on compilation stack • Develop and maintain model definition framework that consists of model building blocks to represent large language models based on PyTorch and Cerebras dialects ready to be deployed on Cerebras hardware. • Develop and maintain the frontend compiler infrastructure that ingests PyTorch models and produces an intermediate representation (IR). • Extend and optimize PyTorch FX / TorchScript / TorchDynamo-based tooling for graph capture, transformation, and analysis. • Collaboration with other teams throughout feature implementation • Research on new methods for model optimization to improve Cerebras inference