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Goodfire

Public-benefit AI interpretability lab building models

Research Lead - Training

AI ResearchConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Experience leading post-training, model training, or closely related efforts in a frontier AI environment.
  • Track record of owning ambitious technical programs that combine research depth with executional rigor.
  • Experience managing teams and operating effectively in fast-moving, high-expectation environments.
  • Strong judgment on how to balance longer-term research bets with near-term product or model delivery.
  • Interest in intentional design, alignment, and interpretability-informed training.
  • Ability to work closely with senior scientific leadership while independently setting direction and driving outcomes.

Responsibilities

  • Lead Goodfire’s training organization and manage a high-performing team from day one.
  • Set direction for model training as Goodfire develops and deploys its own models.
  • Drive execution on post-training and model improvement work, with accountability for shipping high-quality training outcomes.
  • Define and lead a research agenda in intentional design, interpretability-informed training, and alignment.
  • Partner with the Chief Scientist to shape priorities, balance research and delivery, and translate strategy into execution.
  • Build the team over time, including hiring, mentoring, and creating strong technical and managerial foundations.

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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