As an Infrastructure Hardware Technical Program Manager (Server and Network Systems) on the Cluster Architecture Team, you will drive end-to-end delivery of server and network platform programs across Cerebras CS-3-based AI clusters - from requirements and vendor selection through lab bring-up, qualification, and production rollout. You will be the execution owner for multi-team programs spanning OEM/ODM partners, component vendors, internal software/runtime teams and architects, validation/QA, and deployment/operations.
This role is intentionally technical: you must understand server, network, and system-level trade-offs well enough to run effective technical reviews, keep programs grounded in real constraints, and maintain a crisp decision trail - while partnering closely with the Compute / Server / Network Platform Architects for detailed technical direction and sign-off. You will also build shared understanding with our rack/elevations and physical datacenter design partners so that server and network changes land smoothly in real deployments (without owning physical DC design).
Responsibilities
Skills and Qualifications
B.S. or M.S. in Computer Science, Electrical/Computer Engineering, or equivalent experience.
8+ years in Technical Program Management (or similar delivery leadership) for server, network, or infrastructure platforms from concept through production.
Experience coordinating complex server and/or datacenter network programs across OEM/ODMs, switch vendors, and internal engineering teams.
Working knowledge of server architecture (CPU/NUMA, memory bandwidth, PCIe, NIC and storage IO) and enough networking fundamentals (leaf-spine fabrics, switch platforms, high-performance interconnects) to run effective technical reviews.
Familiarity with Linux server fleet management (provisioning, firmware/BIOS, drivers, field triage).
Strong multi-team program execution skills: integrated plans, risk management, dependency tracking, and executive-level communication.
Ability to operate in ambiguity and keep parallel server and network workstreams aligned.
Experience with AI/ML, HPC, or performance-sensitive distributed infrastructure is a plus.