Drimva

Meta

Supply Planner, Reality Labs

$121K–$177K

Sunnyvale, CA · Full-time · Posted May 28, 2026

Experience
4+ years; also lists 5+ years for a profile with direct exposure to technically complex hardware products.
Education
University BS in Supply Chain, Industrial Engineering, Operations Research, or a related field; MS in these fields is also listed.

About the job

Plans end-to-end global supply and product availability for Reality Labs products, optimizing production, capacity, materials, and inventory across the product lifecycle.

What you'll do

  • Create weekly and monthly supply plans, including constrained production schedules, capacity and material plans, and recommendations for S&OP and S&OE.
  • Manage contract manufacturer ship plans, supply issues, inbound priorities, inventory, and launch readiness with cross-functional teams.
  • Model yields, technical product constraints, and supply networks; develop planning automation, optimization models, and tools.

What they ask for

  • 4+ years of experience in Supply Planning; the posting also lists 5+ years for a more specific profile involving technically complex hardware.
  • Experience with S&OP and S&OE, scenario modeling, constrained optimization, and NPI launch planning.
  • Proficiency in ERP systems, Excel, and PowerPoint; experience using data and analytics platforms.
  • BS in Supply Chain, Industrial Engineering, Operations Research, or a related field.

Nice to have

  • Experience with global manufacturing operations and contract manufacturers, sourcing, and technically complex components.
  • Supply chain/materials management certification (e.g., APICS CSCP/CPIM); advanced planning systems or solver-based tool development.

Pay

$121,000/year to $177,000/year USD base salary; bonus + equity (amount not stated)

Tools

ERP systems, Excel, PowerPoint, Tableau, Business Intelligence hubs

AI focus

Integrate AI tools to optimize or redesign workflows and deliver measurable impact; develop AI skills such as prompt/context engineering and agent orchestration; apply responsible AI practices including risk assessment, bias mitigation, and quality and accuracy reviews.

Model-written summary · the original posting is authoritative

First seen Oct 17, 2026 · Last verified Nov 5, 2026 (UTC)