Strategic Events Lead
Goodfire AI· GTM
San Francisco, CAfull-timePosted 3mo ago
About the role
Lead the end-to-end events strategy for Goodfire, designing high-signal, technically-focused experiences (dinners, salons, conference programs, field events, partner gatherings) to engage AI researchers, engineers, CTOs, and other technical buyers, and to generate pipeline and market insights.
Responsibilities
- Own the events strategy and calendar: identify the moments, formats, audiences, and geographies where Goodfire should show up
- Design high-signal experiences: create events that feel substantive to AI researchers, ML engineers, CTOs, Heads of AI, and other deeply technical buyers
- Execute with precision: manage vendors, budgets, run-of-show, speaker prep, attendee experience, follow-up, and every operational detail that determines quality
- Build targeted invite and attendance strategy: partner with GTM, demand gen, and field teams to reach the right accounts and convert attendance into relationship momentum
- Measure and improve the motion: connect events to pipeline, account engagement, market learning, and repeatable playbooks for future programs
Qualifications
- Experience planning and executing high-quality B2B events, field marketing programs, community programs, or executive gatherings
- Strong project management skills and ability to run many operational details without losing sight of strategy
- Excellent taste and audience judgment; ability to make events substantive, polished, and non-generic
- Scrappiness, urgency, and comfort building a function from scratch in a fast-moving environment
Skills
Required:Event strategyProject management
Nice to have:Speaker/program management
Benefits
- market competitive salary
- equity
- competitive benefits
Last updated Sep 15, 2026. Interested? Apply on the company site.
About Goodfire AI
Goodfire AI is an AI interpretability research lab focused on understanding and intentionally designing advanced AI systems.
AI interpretabilityresearchSilicointentional designmodel debugging
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