Cerebras

• Develop design specifications for new machine learning and linear algebra kernels and mapping to the Cerebras WSE System using various parallel programming algorithms. • Develop and debug kernel library of highly optimized low level assembly instruction and C-like domain specific language routines to implement algorithms targeting the Cerebras hardware system. • Develop and debug high-performance kernel routines in low-level assembly and a custom C-like (CSL) language, implementing algorithms optimized for the Cerebras hardware system. • Using mathematical models and analysis to measure the software performance and inform design decisions. • Develop and integrate unit and system testing methodologies to verify correct functionality and performance of kernel libraries. • Study emerging trends in Machine Learning applications and help evolve Kernel library architecture to address computational challenges of the start-of-the-art Neural Networks. • Interact with chip and system architects to optimize instruction sets, microarchitecture, and IO of next generation systems.

• Lead deployment of AI clusters including Cerebras Wafer Scale Engine, high-speed switches, storage, and rack-level infrastructure • Coordinate rack integration of high-density compute, specialized racks, and associated power components (PDUs, busway drops) • Ensure deployment aligns with AI cluster architecture, topology, and scaling requirements • Manage installation of high-speed interconnect cabling (fiber and copper) supporting AI fabrics (east–west traffic) in Data Centers • Coordinate inter-rack and intra-rack cabling for AI clusters, including spine-leaf and pod-level designs • Ensure proper routing, airflow clearance, labeling, and testing of all AI-related cabling • Work closely with facilities teams on power capacity, cooling readiness, containment, and grounding for dense racks • Coordinate deployment sequencing with facility commissioning milestones • Validate white-space readiness before rack and cluster deployment • Troubleshoot cabling, connectivity, and integration issues impacting AI cluster bring-up • Lead root-cause analysis for deployment blockers related to cabling, hardware placement, or facilities dependencies • Support validation, burn-in, and handoff of AI clusters to operations teams • Partner with network, server, AI platform, and operations teams to align on deployment plans and readiness • Manage multiple parallel AI cluster deployments across sites or availability zones • Communicate risks, dependencies, and milestones clearly to stakeholders • Ensure deployments follow company design standards, structured cabling best practices, and AI deployment playbooks • Validate as-built documentation, labeling accuracy, and deployment checklists • Maintain accurate records for cluster configuration, cabling, and deployment status • Enforce EHS, data center safety, and access control procedures during deployment

• Drive end-to-end Data Center Delivery execution across data center builds • 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 (build dashboards)

Role Description The Datacenter Portfolio & Vendor Management Lead will own the operational backbone of Cerebras' global datacenter program. This person partners closely with Engineering, Infrastructure, Finance, Legal, Procurement, Accounting, and Executive Leadership to manage strategic supplier relationships, commercial agreements, budgets, payment operations, and portfolio reporting. This is a highly cross-functional role requiring exceptional organizational skills, commercial judgment, and the ability to drive execution across multiple simultaneous infrastructure programs. Responsibilities - Portfolio Management - Own the centralized portfolio of global datacenter projects - Track capacity deployments, schedules, milestones, risks, and dependencies - Develop executive dashboards and portfolio health reporting - Coordinate planning across Engineering, Operations, Finance, and Supply Chain - Contract Management - Own lifecycle management of datacenter contracts - Coordinate contract negotiations with Legal and Procurement - Track key commercial terms, renewals, amendments, and obligations - Maintain repository of supplier agreements and commercial documentation - Vendor Management - Manage strategic relationships with: - Colocation providers - Construction partners - Power and utility providers - Network providers - Equipment vendors - Professional services partners - Track vendor performance against SLAs and KPIs - Drive quarterly business reviews - Resolve commercial escalations - Financial Operations - Track purchase commitments and contract values - Partner with Finance on forecasting and budget management - Coordinate invoice approvals and payment workflows - Monitor spend against approved budgets - Support capital expenditure planning - Program Operations - Build scalable operational processes for the rapidly growing datacenter organization - Drive governance reviews - Maintain risk registers - Coordinate executive reporting - Improve portfolio management tooling and reporting automation Qualifications - 8+ years managing complex infrastructure, construction, or technology programs - Experience managing large vendor portfolios - Strong contract administration experience - Budget ownership experience ($10M+ preferred) - Experience working with Finance and Legal organizations - Excellent project management skills - Ability to work across highly technical engineering organizations - Outstanding communication and executive presentation skills Preferred Qualifications - Experience with one or more: - Hyperscale datacenters - Cloud infrastructure - AI infrastructure - Colocation providers - Construction management - Critical facilities - Supply chain operations - ERP systems (Oracle, SAP, NetSuite) - Coupa - Jira - Smartsheet - Airtable - Tableau or Power BI Success in this Role Within your first year you will: - Build operational rigor across Cerebras' global datacenter portfolio - Improve visibility into project health and financial performance - Establish scalable vendor governance processes - Streamline contract and payment workflows - Enable infrastructure teams to execute faster by reducing operational overhead - Establish metrics of economic KPI’s in all leases in the portfolio (Contracted vs actual) 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.

• Define and own the architectural roadmap for Cerebras's enterprise, data center, and cloud network estate. • Co-author secure design and lifecycle management of high-performance data center networking — including spine/leaf, RDMA, and high-bandwidth fabrics supporting wafer-scale compute. • Build and maintain an AI-agent based analysis and review framework for network changes as well as self-improvement driven by observed network patterns and use cases. • Partner with Network Security to deliver segmented, zero-trust-aligned network designs across data center and corporate environments. • Set standards for resiliency, observability, and capacity planning across the global network • Mentor engineers across regions (US, Canada, Bangalore, and beyond), and serve as the senior technical voice on cross-functional network initiatives. • Stay ahead of emerging networking patterns relevant to AI infrastructure and translate them into actionable architecture decisions.

• Design and operate firewall, segmentation, and zero-trust controls across data center, corporate, and cloud (AWS) networks. • Build and maintain network security infrastructure as code — including firewall rules, policy automation, and CI/CD-driven deployment. • Lead network lifecycle management: design review, configuration baselines, change automation, and ongoing rule hygiene. • Build detection capabilities for network-based attacker behaviors and partner with the Detection & Response team on response playbooks. • Implement and operate network access controls including ZTNA, VPN, and remote access patterns. • Drive periodic firewall rule reviews, segmentation audits, and remediation campaigns to reduce risk and complexity. • Document network security architecture, controls, and operating procedures in clear runbooks.

• Define security architecture and build controls for AI platforms, training and inference workflows, model-serving systems, customer workloads, developer workflows, and agentic • Develop reusable AI and agent security patterns for identity, authorization, delegated authority, scoped tool access, MCPs, connectors, secrets, approvals, isolation, auditability, and • Design runtime controls that constrain execution, access, data exposure, model and tool interaction, and blast radius. • Build security capabilities as code using infrastructure as code, configuration as code, policy as code, GitOps, CI/CD, and automated validation. • Define secure development patterns for AI systems, agents, prompts, tools, models, policies, evaluations, releases, and rollback. • Automate security reviews, policy checks, evidence collection, control validation, and remediation • Instrument AI, agent, and platform activity with telemetry, traceability, policy decisions, audit logs, anomaly signals, and response workflows. • Lead hands-on security reviews and influence product, platform, infrastructure, and security architecture through practical design changes and reusable controls.

• Partner with platform, infrastructure, and hardware teams to embed security controls from physical hardware through runtime. • Design and implement security hardening across the Linux kernel, bootloader, firmware, and host OS layers of Cerebras compute platforms. • Drive secure boot, measured boot, and attestation strategies across our infrastructure, from the wafer-scale system to supporting host nodes. • Conduct deep security reviews of kernel modules, drivers, and low-level system components — identifying and remediating memory safety, privilege escalation, and isolation issues. • Develop kernel-level monitoring and telemetry (e.g., eBPF) to enable detection of low-level attacker behavior. • Stay ahead of emerging kernel CVEs, supply chain risks, and hardware-level threats — driving response and remediation across the fleet. • Document low-level security posture, threat models, and remediation playbooks in clear, accessible language.

• Own delivery of AI-optimized data center capacity (colo, build-to-suit, retrofits, and owned facilities) from pre-contract planning through operational readiness. • Deliver MW-scale infrastructure aligned to aggressive GPU/AI system deployment targets. • Drive clarity from ambiguity—translate high-level demand signals into executable delivery programs. • Decompose complex build programs into workstreams with clear owners, milestones, and deliverables. • Build integrated plans spanning real estate, power/energy, design, procurement, construction, and deployment. • Establish critical path visibility and aggressively manage schedule compression. • Orchestrate execution across real estate & site selection, power & energy strategy, data center design, supply chain & procurement, construction & commissioning, infrastructure deployment, networking, and security. • Identify and drive resolution of critical risks, constraints, and blockers. • Own and maintain program budgets, CapEx forecasts, and capital allocation narratives. • Partner with capacity planning, AI infrastructure, and finance teams to translate model demand into site-level capacity strategies. • Continuously optimize for time-to-capacity and cost-per-MW / cost-per-GPU deployed. • Drive E2E improvements in delivery through standardization, implementation of program tooling, and post-mortems.

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.