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Finance Manager, MSL FP&A

Job in Menlo Park, San Mateo County, California, 94029, USA
Listing for: Meta
Full Time position
Listed on 2026-09-12
Job specializations:
  • Finance & Banking
    Corporate Finance, Financial Reporting
Salary/Wage Range or Industry Benchmark: 150000 - 210000 USD Yearly USD 150000.00 210000.00 YEAR
Job Description & How to Apply Below

We are looking for a Finance Manager to join the Meta Superintelligence Labs (MSL) Finance team. This role will drive the end-to-end consolidation and FP&A operational processes for Meta's MSL spend, working with finance partners, accounting, and corporate finance to deliver accurate, timely financial reporting and forecasting to senior leadership. This is a high-visibility role working on one of the top priority areas for Meta.

  • 9+ years of experience in FP&A
  • Experience leading close and/or forecast processes in a large, matrixed organization
  • Strong analytical skills with the ability to synthesize large volumes of financial data into clear narratives for senior leadership
  • Ability to prioritize competing work streams based on close cycle deadlines, impact, and stakeholder needs
  • Cross-functional communication skills and ability to drive alignment across teams with competing priorities
  • Advanced proficiency in financial modeling and data analysis tools
  • Ability to work independently with limited guidance, across multiple time zones with both Finance and key cross functional business partners
  • Experience preparing materials for VP-level or C-suite reviews
  • Master's degree in business, finance, accounting, management or related field
  • Experience with Financial systems (e.g. Essbase, Oracle Financials), dashboard reporting tools (i.e. Tableau) and SQL
  • Experience in supporting an AI lab or technology company with a focus on AI
  • Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
  • Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
  • Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
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