General Assembly

General Assembly

Lead Instructor: AI-Enabled Product Marketing Manager

U.S · Director · Part-time

Sponsorship not specified$12k-$16kDetected 78 days ago
AzureAccount ManagementCustomer SuccessMentoring

About the role

  • Since 2011, General Assembly has transformed tens of thousands of careers through pioneering, experiential education in today's most in-demand skills.
  • In addition to fostering career growth for individuals, GA helps employers cultivate top tech talent and spur innovation by transforming their teams through strategic learning.

Responsibilities

  • Facilitate Strategy Workshops: Guide students through hands-on "build-along" activities where they create messaging frameworks, competitive intelligence reports, and launch strategy documents.
  • General Assembly is delivering an intensive reskilling program designed to transition experienced Customer Success and Account Management (CSAM) professionals into AI-Enabled Product Marketing Manager (PMM) roles.
  • As the Lead Instructor, you will be responsible for guiding these professionals as they learn to own the positioning, messaging, and launch strategy for enterprise AI offerings.
  • Lead Live Instruction: Deliver high-energy, synchronous remote lectures on PMM fundamentals, including AI positioning frameworks, messaging, and persona development.

Requirements

  • The Experience: 7+ years in product marketing, with at least 2+ years of recent experience specifically positioning and launching AI-powered products at an enterprise scale.
  • Strategic Mastery: Deep expertise in the "PMM core": positioning, messaging, personas, and go-to-market (GTM) launch motions.
  • The "Teacher" Gene: Proven experience coaching, mentoring, or instructing professionals.
  • Tech Fluency: A strong understanding of the Microsoft AI ecosystem (Copilot, Azure AI, and Foundry) and how these tools compete in the current market.
  • The Credentials: AI-900 certification is required.
  • The instructor must be fully available for 30 hours per week and must operate on Pacific Time (PT) business hours to align with the learner cohort.
  • Proven experience coaching, mentoring, or instructing professionals.
  • AI-900 certification is required.
  • The "Teacher" Gene: Proven experience coaching, mentoring, or instructing professionals. You must be able to translate high-level marketing strategy into digestible, practical steps for a remote cohort.
  • Note on Schedule: This is a high-intensity, 2-week engagement starting in mid-June. The instructor must be fully available for 30 hours per week and must operate on Pacific Time (PT) business hours to align with the learner cohort.
  • Unless otherwise noted, remote positions can be performed from the following approved General Assembly operating countries.
  • United States of America (states of operation may vary), Canada (provinces of operation may vary), United Kingdom, Australia, and Singapore.

Nice to have

  • Experience as a Senior PMM or Director of PMM at a major tech firm (Microsoft, Google, etc.) is a significant plus.
  • The Pedigree: Experience as a Senior PMM or Director of PMM at a major tech firm (Microsoft, Google, etc.) is a significant plus.

Skills

  • Our global professional community boasts 60,000 full- and part-time alumni - and counting.
  • More than 21,000 employees at elite companies worldwide have honed their digital fluency with our upskilling and reskilling initiatives.
  • GA is at the leading edge of creating practical solutions to one of the most pressing challenges of our time - the future of work.
  • A strong understanding of the Microsoft AI ecosystem (Copilot, Azure AI, and Foundry) and how these tools compete in the current market.

Compensation

  • $11,500 - $15,500 (Estimated lump sum payment for one 60-hour program)

Company info

  • General Assembly
  • Client: Confidential - Customer Success Reskilling
  • Duration: 2 Weeks (Starting Mid-June)
  • Commitment: Roughly 30 hours per week

This listing is sourced directly from General Assembly's careers page and normalized into a canonical job model.