Platform Engineer

📋 Job Overview

Goodfire is an AI interpretability research lab. We build interpretability agents and infrastructure to understand, monitor, and align AI models.

We believe that AI is the most consequential technology of our time. Yet AI models are trained by trial and error today, with limited understanding of what drives their behavior. Our goal is to advance the research and technology needed to fully understand and align AI so humanity can trust superintelligence.

Goodfire is a public benefit corporation founded by researchers who helped pioneer the field of interpretability at OpenAI and Google DeepMind. We are headquartered in San Francisco and have raised over $200M from leading investors including Lightspeed, Menlo, and B Capital.

🏢 About Goodfire

Goodfire is an AI interpretability research lab. We build interpretability agents and infrastructure to understand, monitor, and align AI models.

We believe that AI is the most consequential technology of our time. Yet AI models are trained by trial and error today, with limited understanding of what drives their behavior. Our goal is to advance the research and technology needed to fully understand and align AI so humanity can trust superintelligence.

Goodfire is a public benefit corporation founded by researchers who helped pioneer the field of interpretability at OpenAI and Google DeepMind. We are headquartered in San Francisco and have raised over $200M from leading investors including Lightspeed, Menlo, and B Capital.

🎯 The Role

We're looking for Platform 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.

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.

We'll work with you to determine the team and product area that best aligns with your strengths.

✅ Key Responsibilities

• Turn cutting-edge interpretability research into production-ready product features: partner with researchers and ML engineers to make new capabilities usable in the product
• Build high-quality product experiences: own full-stack features, APIs, workflows, and interfaces from ambiguous idea to shipped product
• Create product systems that are reliable and fast: ensure the product is performant, reproducible, observable, and stable enough for real customer use
• Shape product direction through engineering judgment: identify obvious fixes, propose better workflows, and help decide what Goodfire should build next

📌 Required Qualifications

• 2+ years of experience building production software, especially user-facing products, data-intensive systems, or AI/ML products.
• Strong engineering fundamentals and ability to work across the stack, with depth in at least one of frontend, backend, systems, or product infrastructure.
• Strong product judgment; you care about making powerful technical systems feel clear, reliable, and easy to use.
• Comfort working across research, engineering, design, and customer-facing teams.
• You care about understanding how models work internally and using that understanding to make AI systems more reliable and useful in the real world.

⭐ Desirable Experience

• Experience building products for technical users, developers, researchers, ML engineers, or infrastructure teams.
• Startup or frontier lab experience in fast-moving teams.

🎁 Benefits

This role offers market competitive salary, equity, and competitive benefits.

🛂 Visa & Eligibility

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 per month.