{"title": "Infra Hardware TPM - AI Cluster", "level": "", "location": "Bellevue, WA | Austin, TX | Menlo Park, CA", "description": "Meta is seeking a Technical Program Manager (TPM) experienced in managing large-scale AI cluster design, development and deployment. This position will work with cross-functional teams in Meta\u2019s Infrastructure organization to build Large-scale AI clusters that enable Meta\u2019s AI applications and use cases. This position would focus on creating strategies and executing plans to enable Meta\u2019s various AI software workloads to get onboarded on full stack hardware in large-scale AI clusters. This role would be responsible for successful design, build-out, delivery and turn-up of large-scale AI clusters end-to-end; this includes influencing network topology, determining the most appropriate hardware infrastructure in terms of Compute, Storage and Network and how they work together as a solution, delivering such hardware into data center, influencing orchestration system and cluster level software tooling and provisioning, driving cluster-level testing, optimizing cluster-level software performance, and/or migrating existing software applications to enable cluster-level turn-up for AI applications and use cases.", "salary": "US$195,000/year to US$267,000/year", "key_qualifications": "B.S. in Computer Science, Electrical Engineering or a related technical discipline, or equivalent experience. 10+ years of systems engineering, hardware engineering, software engineering, or technical product/program management experience. Understanding of AI hardware stack, bottleneck and dependencies in a cluster setting. Experience delivering complex tech programs and/or products from inception to delivery. Knowledge of user needs, gathering requirements, and defining scope. Experience operating autonomously across multiple teams, demonstrated critical thinking, and thought leadership. Communication experience and experience working with technical management teams to develop systems, solutions, and products. Organizational, coordination and multi-tasking experience. Analytical and problem-solving experience with large-scale systems. Experience establishing work relationships across multi-disciplinary teams and multiple partners in different time zones.", "preferred_qualifications": "Knowledge of storage and networking hardware and technologies. Experience in large scale AI cluster build out. Experience with data center architecture and deployment. Experience working with capacity planning, migration and turn-up. Web or Internet start-up environment and technical infrastructure management experience.", "responsibilities": "Own overall program success spanning large-scale AI cluster build-out end-to-end from concept to full production in a matrix organization covering a range of areas (Infrastructure Hardware development, Infrastructure software, Capacity Planning, Data Center, Network Infrastructure and Infrastructure sourcing teams.) and across multiple physical locations. Create and gather alignment across multiple organizations regarding the business value of specific large-scale AI clusters. Value proposition include adopting new technologies and enabling AI use cases. Create execution plan, including influencing network topology, determining the most appropriate hardware infrastructure in terms of Compute, Storage and Network and how they work together as a solution. Deliver full stack hardware (Compute, Storage, Network, Silicon etc) into the data center and optimizing software performance and large scale testing. Influencing orchestration of system and cluster level software tooling and provisioning. Lead migration of existing software applications to enable cluster-level turn-up for AI applications and use cases. Drive overall communication to leadership, stakeholder and core working teams in regular cadence to bring awareness. Develop and manage the overall program including defining scope, requirements, development model, schedules, and deliverables with engineering teams, partners, and stakeholders. Provide hands-on program management during analysis, benchmarking, design, development, testing, implementation, and post implementation phases. Perform risk assessment, risk mitigation and change management on programs. Drive internal process improvements across multiple teams and functions.", "company": "meta"}