Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; 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.
Cerebras works with the leading 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.
About the Team
The Cerebras Data Center team brings the infrastructure behind our AI systems online. We work across facility design, power and cooling, hardware, networking, supply chain, deployment, and operations to deliver reliable capacity. As our infrastructure expands, we are building the programs and repeatable processes that help teams execute across multiple sites.
About the Role
Cerebras is seeking a Staff Technical Program Manager to lead Data Center New Product Introduction (NPI), preparing new AI systems and infrastructure designs for reliable deployment at scale. You will connect hardware and systems engineering with facility design, supply chain, networking, commissioning, and operations to turn new technical requirements into a repeatable deployment approach.
You will own the integrated readiness program from early requirements and initial deployments through operational acceptance and broader rollout. Success means identifying integration risks early, closing readiness gaps, and ensuring that each new introduction can be deployed and supported consistently across sites.
Responsibilities
Own the cross-functional NPI plan for new system generations, rack configurations, and supporting infrastructure, with clear milestones, owners, dependencies, and readiness criteria.
Translate engineering specifications into deployment requirements covering power, cooling, space, rack layout, networking, cabling, installation, serviceability, and site interfaces.
Drive design and readiness reviews with engineering and deployment teams; surface gaps between the product design, facility capabilities, and operational requirements.
Coordinate initial deployment and validation plans, including site readiness, equipment availability, installation sequencing, test coverage, issue triage, and acceptance evidence.
Maintain the integrated risk and issue register; drive resolution of compatibility, quality, configuration, and schedule blockers with accountable engineering owners.
Lead change control across specifications, bills of materials, firmware and software dependencies, deployment procedures, and site requirements; assess downstream impacts before rollout.
Define repeatable deployment packages with the technical teams, including approved configurations, acceptance checklists, runbooks, training, spares requirements, and operational handoff.
Drive readiness recommendations and executive escalations; incorporate lessons from initial deployments into subsequent rollouts and track improvements in deployment cycle time and issue recurrence.
Required Qualifications
Experience leading complex NPI, systems integration, hardware infrastructure, or deployment programs from early requirements through production or operational readiness.
Track record of Staff-level program ownership across engineering and operations teams, including resolving ambiguous requirements and driving decisions across organizational boundaries.
Technical understanding of data center systems and the relationships among compute, networking, power, cooling, rack integration, and facility readiness.
Experience defining readiness gates, validation plans, acceptance criteria, and issue resolution processes for new technology introductions.
Ability to manage configuration dependencies, engineering changes, critical paths, and technical risks across several concurrent workstreams.
Strong communication and documentation skills, including explaining technical tradeoffs, presenting launch risks, and securing clear decisions from senior stakeholders.
Preferred Qualifications
Experience deploying AI accelerators, high-performance computing systems, high-density racks, or liquid-cooled infrastructure.
Experience with initial site deployments, rack integrators, commissioning teams, and transitions from engineering validation to repeatable field execution.
Familiarity with hardware product lifecycles, manufacturing readiness, reliability validation, and service operations; ability to coordinate dependencies with those teams.
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.
Find out more about what it's like to work at Cerebras here!
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.
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Job details are sourced from the employer's original posting.
Open job postingAbout the company
Cerebras Systems is building a new class of computer to accelerate artificial intelligence work by training neural networks much faster than conventional systems.