MachineLearningEngineer

About Goodfire Goodfire is a research company using interpretability to understand, learn from, and design AI systems. Our mission is to build the next generation of safe and powerful AI—not by scaling alone, but by understanding the intelligence we're building. Scaling has proven powerful, but today's approach is fundamentally limited: we can't meaningfully understand, debug, or shape what models learn. Every engineering discipline has been gated by fundamental science and AI is at that inflection point now. We're advancing the science of how AI systems actually work. Treating models as black boxes is an unnecessary handicap—we have access to the structures inside them, and understanding those structures lets us steer what models learn, make them safer and more useful, and extract the vast knowledge they contain. Our goal is to make AI that can be understood, debugged, and shaped like software. Goodfire is a public benefit corporation headquartered in San Francisco with a team of the world’s top interpretability researchers and engineers from organizations like OpenAI and DeepMind. We're backed by over $200M from B Capital, Menlo Ventures, Lightspeed, Eric Schmidt, and others. About the role We’re looking for Machine Learning Engineers to help build our platform for training, evaluating, and deploying interpretable AI systems at scale. You’ll play a central role in building our core technology, from training and eval tooling to product features, to achieve our mission of understanding and intentionally designing AIs. Where you might contribute: Interpretability tools – Building the tools and infrastructure to support understanding and intentional design of models at industry scale. Training infrastructure – Extending and supporting our training infrastructure for large training runs. Product – Turning state of the art interpretability research into robust, usable product features. We'll work with you to determine the team that best aligns with your strengths. Key r