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Goodfire

Public-benefit AI interpretability lab building models

Demand Generation Lead

Growth MarketingConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Demonstrated ability to build pipeline selling to technical or research-oriented buyers.
  • Experience designing and running multi-channel campaigns across outbound, events, account-based marketing, paid media, and content, and measuring their impact on pipeline and revenue.
  • Ability to operate in ambiguity in a new market with novel buyers and no established playbook.

Responsibilities

  • Build and own a highly personalized demand-generation engine by designing, executing, and optimizing programs across outbound, strategic events, social, and high-value content to engage frontier model builders before their first call.
  • Run rigorous demand-generation experiments by forming hypotheses, launching tests, analyzing results, and scaling what works.
  • Construct highly targeted outreach by using product signals, research engagement, event attendance, and market intelligence to build precise prospect lists and tailor messaging to technical buyers.
  • Build the tools, workflows, and reporting infrastructure needed to run a data-driven demand-generation function.

Desired Qualifications

  • Experience as an early demand-generation or growth hire at a high-growth startup, building the function from scratch.
  • Familiarity with selling to AI/ML teams, research organizations, or deeply technical enterprise buyers.
  • Experience using product-led growth signals or community engagement data to identify high-intent prospects.

About the Company

Goodfire

Public-benefit AI interpretability lab building models

Goodfire is a public-benefit AI interpretability lab in San Francisco that provides a model-design environment to understand, edit, debug, and monitor foundation models rather than treating them as black boxes. Its platform lets teams inspect model decisions, apply interpretability-informed training to reduce errors, and test and monitor changes across deployments. It differentiates itself through its public-benefit mission, collaborations with Arc Institute, Mayo Clinic, and Microsoft, and leadership with researchers, having halved LLM hallucinations and reverse-engineered a biological model to identify Alzheimer's biomarkers. Its goal is to make foundation models safer, more understandable, and easier to manage at scale by promoting transparency and accountability in AI development and deployment.

Company size
51-200
Company stage
Series B
Total funding
$207M
Headquarters
San Francisco, CA
Founded
2024

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