GPU Program Manager, Staff

Qualcomm•San Diego, CA

2d•Onsite

About The Position

Own GPU engineering operations as a program and drive PMO strategic initiatives by building capacity/cost models, running planning cadences and quality gates, leading analytics/AI enablement initiatives, and maturing product planning operations and PMO processes. This role is based in San Diego, CA and requires full-time onsite work (5 days a week). #What you will be doing - Run GPU engineering operations as an operational “control plane” for planning cadences, decision-ready reviews, and business-partner communications in a matrixed environment. - Build and operate the capacity + NRE modeling engine: demand–capacity gap analysis, person‑month cost profiles, and what‑if scenarios that quantify tradeoffs and drive decisions (with assumptions logs and quality gates). - Own recurring planning and review forums (forecast cadence, internal spend/capacity reviews, program and business-partner reviews), including preparation, controls, and follow-through. - Create and maintain a scalable system to track program attributes, assumptions, and NRE profiles used in planning and reviews (repeatable and audit-ready). - Own product planning operations using Agile practices (process governance, workflow discipline, and continuous improvement including Kanban/WIP limits). - Drive PMO maturity: tighten SOPs, templates, operating mechanisms, and quality controls; improve onboarding/training/documentation systems. - Lead strategic PMO initiatives (unified analytics/data foundation; tracking and operationalization of engineering AI/GenAI efficiency initiatives) with measurable adoption and outcomes. - Manage and coach a small team of direct reports (Engineering Ops Analyst(s), Data Analyst, Technical Writer) in a hands-on, player‑coach model.

Requirements

Bachelor’s degree in Engineering, Computer Science, or related field (or equivalent experience).
15+ years of experience in program management / engineering operations / PMO leadership in complex engineering organizations.
Proven experience building and running planning cadences, governance controls, and decision-ready outputs (assumptions/version control, quality gates, repeatable SOPs).
Strong Agile/Scrum experience; 5+ years using Agile planning/execution tools (e.g., Jira).
Demonstrated ability to build models and scenario analysis that drive decisions (capacity/demand, cost, tradeoffs).
Experience leading teams with direct reports and enforcing high standards of quality, clarity, and execution.

Nice To Haves

PMP and/or Agile certification (CSM/SAFe/Scrum).
Experience with ServiceNow SPM.
Strong analytics mindset and hands-on comfort with dashboards/BI (Power BI/Tableau) and automation of reporting workflows.
Experience driving or operationalizing engineering AI/GenAI efficiency initiatives (tracking adoption, outcomes, and governance).

Responsibilities

Run GPU engineering operations as an operational “control plane” for planning cadences, decision-ready reviews, and business-partner communications in a matrixed environment.
Build and operate the capacity + NRE modeling engine: demand–capacity gap analysis, person‑month cost profiles, and what‑if scenarios that quantify tradeoffs and drive decisions (with assumptions logs and quality gates).
Own recurring planning and review forums (forecast cadence, internal spend/capacity reviews, program and business-partner reviews), including preparation, controls, and follow-through.
Create and maintain a scalable system to track program attributes, assumptions, and NRE profiles used in planning and reviews (repeatable and audit-ready).
Own product planning operations using Agile practices (process governance, workflow discipline, and continuous improvement including Kanban/WIP limits).
Drive PMO maturity: tighten SOPs, templates, operating mechanisms, and quality controls; improve onboarding/training/documentation systems.
Lead strategic PMO initiatives (unified analytics/data foundation; tracking and operationalization of engineering AI/GenAI efficiency initiatives) with measurable adoption and outcomes.
Manage and coach a small team of direct reports (Engineering Ops Analyst(s), Data Analyst, Technical Writer) in a hands-on, player‑coach model.

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