Meta
Product Marketing Manager
$130K–$188K
Menlo Park, CA +2 · Manager · Full-time · Posted Jun 24, 2026
- Location
- Menlo Park, CA
New York, NY
San Francisco, CA - Experience
- 8+ years in ad-tech, strategic marketing, product management, or management consulting within the technology sector.
- Education
- Bachelor's Degree in Business, Marketing or Creative discipline; MBA or other relevant advanced degree is also listed.
About the job
Drives market requirements and go-to-market strategy for Meta advertiser products, using market research and cross-functional coordination to guide product development, launches, and growth.
What you'll do
- Assess advertiser needs and market feedback, conduct quantitative and qualitative analysis, and inform product, engineering, and design teams.
- Coordinate cross-functional product initiatives and align regional and vertical teams around consistent solutions that address market-specific needs.
- Own go-to-market strategy, including positioning, communications, documentation, messaging, and sales and partnership team enablement.
What they ask for
- 8+ years of experience in ad-tech, strategic marketing, product management, or management consulting within the technology sector.
- Experience collaborating across cross-functional teams and gaining leadership support to resolve competing perspectives.
- Analytical, data-driven decision-making and effective communication with technical, business, and external stakeholders.
- Bachelor's Degree in Business, Marketing or Creative discipline.
Nice to have
- MBA or other relevant advanced degree.
- Experience coordinating global go-to-market end-to-end, including sales enablement and positioning/narrative; product marketing experience supporting launches is also cited.
Pay
$130,000/year to $188,000/year USD base salary; bonus and equity also stated, amounts unspecified.
AI focus
Integrate AI tools to optimize or redesign workflows and deliver measurable impact; continue developing AI skills (such as prompt/context engineering and agent orchestration) and apply responsible AI practices, including risk assessment, bias mitigation, and quality and accuracy reviews.
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