Drimva

Meta

Principal Program Manager, Infrastructure Development

$202K–$273K

Menlo Park, CA +2 · Remote · Principal · Full-time · Posted Jul 8, 2026

Location
Menlo Park, CA
Seattle, WA
Remote, US
Experience
15+ years.
Education
Bachelor's degree in a directly related field, or equivalent practical experience. The posting also specifies a bachelor's degree in engineering, business, construction management, or a related field.

About the job

Leads end-to-end program execution, governance, risk management, and partner coordination for Meta’s energy infrastructure investment portfolio.

What you'll do

  • Manage portfolio plans, schedules, stage gates, reporting, risks, and cross-project dependencies.
  • Establish governance and operating rhythms, and coordinate delivery across internal teams and external energy partners.
  • Track capital deployment, forecasts, contract and regulatory milestones, and improve program tools and processes.

What they ask for

  • 15+ years of program management in energy infrastructure, power development, data center construction, or large-scale capital project delivery.
  • Track record managing complex, multi-billion-dollar infrastructure programs and partnerships with utilities, independent power producers, or developers.
  • Deep knowledge of energy infrastructure development lifecycles, including permitting, interconnection, construction, and commissioning.
  • Bachelor’s degree in a directly related field, or equivalent practical experience.

Nice to have

  • Experience standing up program management functions in fast-scaling organizations; MBA, PMP, or other relevant advanced degree or credential.
  • Experience in hyperscale data center or cloud energy delivery, investment or project finance, utility interconnection, or power purchase agreements.

Pay

$202,000/year to $273,000/year (base salary)

AI focus

Integrate AI tools to optimize or redesign workflows and drive measurable impact; ongoing AI skill development, including prompt/context engineering and agent orchestration; implement responsible AI practices such as risk assessment, bias mitigation, and quality and accuracy reviews.

Model-written summary · the original posting is authoritative

First seen Oct 15, 2026 · Last verified Nov 4, 2026 (UTC)