We are seeking a Technical Program Manager to lead recurring capacity operations for large-scale AI infrastructure programs and support tooling and automation initiatives! You will build the mechanisms that keep demand, supply, readiness, risks, decisions, and delivery work visible, sequenced, detailed, and moving.
This is an operational leadership role, not a meeting-coordination role. You will challenge incomplete inputs, establish operating cadences, turn ambiguous constraints into clear decisions, and close cross-functional actions - partnering with, not replacing, technical owners.
What you will be doing:
Own intake, triage, routing and request-quality standards for accelerator-capacity requests across multiple engineering and research teams.
Run quarterly and annual demand forecasts, change control, review preparation, and follow-through; identify late, incomplete, or conflicting inputs early.
Prepare capacity-planning and allocation reviews using demand, available supply, prior commitments, readiness, workload timing, and business priorities.
Track capacity through its operational lifecycle—from request and forecast through delivery, readiness, assignment, and productive use.
Coordinate infrastructure dependencies such as access, storage, data movement, networking, readiness checks, migration timing, and provider or provisioning tickets.
Maintain dashboards and source-data quality, including freshness checks, ownership for missing inputs, reconciliation, and retirement of repeated manual reporting.
Own operating cadences, agendas, action logs, dependency tracking, decision records, risk registers, blocking issue paths, and closure difficulty.
Prepare concise leadership reporting that distinguishes facts, risks, decisions needed, options, recommended paths, accountable owners, and due dates.
What we need to see:
BS/MS/PhD in Electrical Engineering, Computer Science, Computer Engineering or similar (or equivalent experience).
7+ years of technical program management or closely related experience in AI/ML platforms, distributed systems, cloud infrastructure, compute capacity, or another technically demanding engineering environment.
Shown ownership of recurring operational programs with scarce-resource trade-offs, multiple cadences, executive clarity, and overlapping peak periods.
Strong program mechanics: intake compose, forecasting, review preparation, dependency management, decision and risk records, action closure, documentation, and status communication.
Enough technical proficiency to understand infrastructure constraints, ask detailed questions, inspect requirements and metrics, and work credibly with engineers and researchers.
Data proficiency sufficient to evaluate source quality, interpret dashboards, reconcile conflicting views, define useful operational measures, and identify missing ownership.
Consistent track record to transform ambiguous cross-functional work into a durable operating system with clear owners, decisions, and critical issue paths with excellent written and verbal communication, including the ability to turn sophisticated updates into concise decisions, risks, options, actions, and leadership mentorship.
Ability to influence without authority across research, platform engineering, infrastructure, data, finance, operations, and leadership collaborators!
Ways to stand out from the crowd:
Experience with GPU or other accelerator-capacity planning, utilization programs, cluster operations, workload bring-up, or large-scale AI training and inference environments.
Experience with demand and supply roadmaps, normalization across accelerator types, allocation reviews, quota or priority management, and capacity migrations.
Familiarity with batch scheduling, cluster-management, cloud, or data-center environments and the operational dependencies that affect workload readiness.
Experience with observability or dashboard platforms, data-quality controls, and automated reporting for infrastructure or service operations.
Experience driving API-enabled workflow automation or decision-support tools with explicit human approval gates, audit evidence, and safe operating controls.
You will also be eligible for equity and benefits.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.#deeplearningSkills Required
- BS, MS, or PhD in Electrical Engineering, Computer Science, Computer Engineering, or a similar field; equivalent experience may be accepted
- 7+ years of technical program management or closely related experience
- Experience in AI/ML platforms, distributed systems, cloud infrastructure, compute capacity, or another technically demanding engineering environment
- Ownership of recurring operational programs involving scarce-resource trade-offs, multiple cadences, executive clarity, and overlapping peak periods
- Strong program mechanics, including intake, forecasting, review preparation, dependency management, decision and risk records, action closure, documentation, and status communication
- Technical proficiency sufficient to understand infrastructure constraints, inspect requirements and metrics, and work credibly with engineers and researchers
- Data proficiency sufficient to evaluate source quality, interpret dashboards, reconcile conflicting views, define operational measures, and identify missing ownership
- Excellent written and verbal communication, including the ability to turn complex updates into concise decisions, risks, options, actions, and leadership reporting
- Ability to influence without authority across research, platform engineering, infrastructure, data, finance, operations, and leadership collaborators
- Experience with GPU or accelerator-capacity planning, utilization programs, cluster operations, workload bring-up, or large-scale AI training and inference environments
- Experience with demand and supply roadmaps, accelerator normalization, allocation reviews, quota or priority management, and capacity migrations
- Familiarity with batch scheduling, cluster-management, cloud, or data-center environments and workload-readiness dependencies
- Experience with observability or dashboard platforms, data-quality controls, and automated reporting for infrastructure or service operations
- Experience driving API-enabled workflow automation or decision-support tools with human approval gates, audit evidence, and safe operating controls
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
