Technical Program Manager, Supercomputing

Description

The company builds frontier AI models and products used by millions of people, covering pretraining, post-training, evaluation and launch. The infrastructure behind this work is very large, so datacenter buildout, capacity delivery, cluster bring-up, platform readiness and production operations must be tightly coordinated.

This role organizes and drives the full set of programs needed to deliver and run the large-scale AI infrastructure that supports the team.

Work is organized as one lifecycle: Buildout, then Bring-up, then Operationalize.

You will work with the AI leadership group, Supercomputing engineering teams, capacity and infrastructure owners, and partner organizations at Azure. You will turn strategy and capacity needs into coordinated work across delivery, cluster readiness, validation and production operations.

You will set up operating mechanisms that link priorities, execution, risks, dependencies and decisions across Supercomputing programs.

You need enough technical depth to speak credibly about datacenter infrastructure, compute, GPU systems, networking, storage, cluster bring-up, schedulers, platform services and production operations, plus the program skills to lead complex work across organizational lines.

Responsibilities

• Help design the program structure and operating rhythm that connect AI infrastructure priorities to delivery.
• Organize programs around the capacity lifecycle, from datacenter and infrastructure buildout through cluster bring-up and validation to production operations and ongoing health.
• Set milestones, owners, dependencies, readiness criteria, risks and decision points for each stage, so leadership can see whether capacity will arrive on time and be usable by AI workloads.
• Lead the relationship between the AI team and Azure. Build ties across Azure partner groups, set up engagement and escalation paths, clarify cross-organizational dependencies, and make sure AI requirements shape joint planning and delivery.
• Connect priorities to the Supercomputing operating cadence, help teams find systemic gaps and resolve cross-team dependencies, and keep owners accountable for key outcomes.
• Own the end-to-end Supercomputing program portfolio across Buildout, Bring-up and Operationalize, with a clear path from delivery through production readiness to ongoing operations.
• Turn AI strategy, model roadmaps and capacity requirements into Supercomputing priorities, milestones, dependencies and execution plans.
• Build the program structure linking datacenter buildout, capacity delivery, hardware and network readiness, cluster bring-up, validation, workload certification and production handover.
• Define readiness gates and exit criteria between Buildout, Bring-up and Operationalize, so capacity moves predictably between teams and stages.
• Drive execution across Supercomputing, AI Infrastructure, capacity, datacenter, compute, networking, storage, scheduling, platform and operations teams.
• Lead Azure partner work for Supercomputing, including operating rhythms, ownership models, escalation paths, dependency tracking and joint planning between the AI team and Azure.
• Represent AI requirements in Azure infrastructure discussions, and make sure partner commitments, risks and dependencies are visible to Supercomputing leadership.
• Maintain an integrated view of capacity, milestones, readiness, risks, dependencies and operational health, so leadership can quickly see where they must step in or decide.
• Find cross-program risks and systemic bottlenecks across buildout, cluster readiness, capacity availability, validation and production operations before they escalate.
• Set up executive mechanisms: portfolio reviews, milestone and readiness reviews, risk reviews, partner reviews and leadership decision forums.
• Make sure decisions lead to clear owners, actions and measurable results.
• Align MAI and Azure teams on shared infrastructure and capacity outcomes, although you do not manage them directly.
• Report Supercomputing strategy, portfolio health, key milestones, risks, trade-offs and decisions to senior leadership briefly and with clear next steps.

Requirements

• Significant experience in technical program management, infrastructure engineering, capacity delivery, datacenter infrastructure, HPC, AI infrastructure or large distributed systems.
• You have led large, complex portfolios across several engineering and infrastructure organizations.
• You have run programs across several stages of an infrastructure lifecycle, such as capacity planning, datacenter buildout, delivery, cluster bring-up, validation, production readiness and operations.
• You understand large-scale AI and HPC infrastructure, including accelerators, compute, networking, storage, schedulers, capacity, telemetry, datacenter infrastructure and distributed systems.
• You can turn organizational strategy and technical needs into structured programs, milestones, dependencies, readiness criteria and execution mechanisms.
• You have managed complex dependencies across organizations where ownership is spread over several teams.
• You build relationships and influence engineering and infrastructure teams without formal authority.
• You have set up executive operating rhythms, portfolio reviews, risk management and decision frameworks.
• You communicate well in writing and speaking, turning complex infrastructure topics into outcomes, risks, decisions, ownership and actions for leadership.
• Preferred: experience delivering GPU or accelerator-based AI or HPC capacity at datacenter or fleet scale.
• Preferred: experience coordinating programs across datacenter construction, hardware deployment, networking, storage, cluster bring-up, validation and production operations.
• Preferred: experience working with large cloud provider organizations on infrastructure delivery, capacity management or production readiness.
• Preferred: experience setting up portfolio-level mechanisms for capacity planning, cluster readiness, utilization, reliability and operational health.
• Preferred: experience as a TPM for a technically complex engineering or infrastructure organization.

Conditions

Technical Program Management, IC6 level.

Base pay across the U.S. is USD $165,600 to $296,400 per year.

In the San Francisco Bay Area and New York City metropolitan area, base pay is USD $220,800 to $331,200 per year.

Some roles may be eligible for benefits and other pay. Details are available from the employer's benefits page.

The role stays open for at least 5 days and accepts applications on an ongoing basis until filled.

Benefits

• Some roles may be eligible for benefits and other compensation.