Goodfire is a San-Francisco-based AI research lab that leverages interpretability techniques to design safer, more controllable models and unlock scientific discoveries.
<div class="content-intro"><h2><strong>About Goodfire</strong></h2> <p>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.<br><br>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.</p> <p>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.</p> <p>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.</p></div><h2>About the role</h2> <p>We're looking for Product Engineers to help build the core product experience for training, evaluating, debugging, and deploying interpretable AI systems at scale. You'll play a central role in turning Goodfire's research and platform capabilities into products that people can actually use: clear interfaces, reliable workflows, developer tools, and product surfaces that make model internals understandable and actionable.</p> <p>This role is similar to our Machine Learning Engineer role, but with a stronger focus on core product building. You will work across engineering, design, research, and field teams to translate state-of-the-art interpretability into robust product features, from early prototypes through production systems used by customers.</p> <h3><strong>Where you might contribute</strong></h3> <ul> <li>Product surfaces: build the interfaces, workflows, and developer experiences </li> <li>Research-to-product translation: turn new interpretability techniques into usable features that are reliable, explainable, and easy to adopt</li> <li>Customer workflows: build product paths that support real-world use cases across evaluation, model understanding, intervention, monitoring, and deployment</li> <li>Platform integration: connect product features to the underlying infrastructure for model analysis, training, inference, and experimentation</li> </ul> <p>We'll work with you to determine the team and product area that best aligns with your strengths.</p> <h3><strong>Key responsibilities</strong></h3> <ul> <li>Turn cutting-edge interpretability research into production-ready product features: partner with researchers and ML engineers to make new capabilities usable in the product</li> <li>Build high-quality product experiences: own full-stack features, APIs, workflows, and interfaces from ambiguous idea to shipped product</li> <li>Create product systems that are reliable and fast: ensure the product is performant, reproducible, observable, and stable enough for real customer use</li> <li>Shape product direction through engineering judgment: identify obvious fixes, propose better workflows, and help decide what Goodfire should build next</li> </ul> <h2><strong>What you’ll bring</strong></h2> <h3><strong>Required experience</strong></h3> <ul> <li>2+ years of experience building production software, especially user-facing products, data-intensive systems, or AI/ML products.</li> <li>Strong engineering fundamentals and ability to work across the stack, with depth in at least one of frontend, backend, systems, or product infrastructure.</li> <li>Strong product judgment; you care about making powerful technical systems feel clear, reliable, and easy to use.</li> <li>Comfort working across research, engineering, design, and customer-facing teams.</li> <li>You care about understanding how models work internally and using that understanding to make AI systems more reliable and useful in the real world.</li> </ul> <h3><strong>Preferred qualifications</strong></h3> <ul> <li>Experience building products for technical users, developers, researchers, ML engineers, or infrastructure teams.</li> <li>Startup or frontier lab experience in fast-moving teams.</li> <li>Startup or frontier lab experience in fast-moving teams.</li> </ul> <h2>Our values</h2> <p>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.</p> <p><strong>Put mission and team first</strong></p> <p>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.</p> <p><strong>Improve constantly</strong></p> <p>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.</p> <p><strong>Take ownership and initiative</strong></p> <p>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.</p> <p><strong>Action today</strong></p> <p>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 do it today.</p> <h2>Where we work</h2> <p>We are hiring for this position in our San Francisco HQ. We are in person 5 days a week, with one company-wide remote week p