Machine Learning Engineer

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 responsibilities: Turn cutting edge interpretability research into production ready tools. Optimize pipelines and infrastructure for frontier model interpretability, training, and inference. Integrate new machine learning workflows and pipelines into our product and deploy to customers. Ensure system reliability, reproducibility, and performance. What you’ll bring Required experience 5+ years of experience in ML infra, research engineering, or systems programming. Comfort working across research and engineering boundaries. Expertise in Python, PyTorch or Jax, and distributed systems. Experience deploying and maintaining ML systems at scale. You care about understanding how models work internally and using that to make them more reliable and useful in the real world. Preferred qualifications Open-source ML infra contributions. Startup or frontier lab experience in fast-moving teams. Our values Goodfire is looking for individuals who embody our values and share our deep commitment to making interpretability accessible. We are building a team first and foremost. Put mission and team first All we do is in service of our mission. We trust each other, deeply care about the success of the organization, and choose to put our team above ourselves. Improve constantly We are constantly looking to improve every piece of the business. We proactively critique ourselves and others in a kind and thoughtful way that translates to practical improvements in the organization. We are pragmatic and consistently implement the obvious fixes that work. Take ownership and initiative There are no bystanders here. We proactively identify problems and take full responsibility over getting a strong result. We are self-driven, own our mistakes, and feel deep responsibility over what we’re building. Action today We have a small amount of time to do something incredibly hard and meaningful. The pace and intensity of the organization is high. If we can take action today or tomorrow, we will choose to