Platform Engineer: Scale Interpretable AI Platforms

Goodfire; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Benefits offered by this job

Equity

Competitive benefits

Job summary

Goodfire is seeking Platform Engineers to build the core product experience for training, evaluating, debugging, and deploying interpretable AI systems at scale. You will turn Goodfire's research and platform capabilities into usable products with clear interfaces, dev tools, and surfaces that make model internals understandable.

This role sits at the intersection of research and product, requiring collaboration across engineering, design, research, and field teams to translate state-of-the-art

Qualifications

• 2+ years building production software, especially user-facing products, data-intensive systems, or AI/ML products.
• Strong engineering fundamentals; ability to work across the stack (frontend, backend, systems, or product infrastructure).
• Strong product judgment; aim for powerful, clear, reliable systems.
• Comfort collaborating across research, engineering, design, and customer-facing teams.
• Interest in understanding models to improve reliability and usefulness in real-world AI systems.

Responsibilities

• Turn cutting-edge interpretability research into production-ready product features; partner with researchers and ML engineers.
• Own full-stack features, APIs, workflows, and interfaces from idea to shipped product.
• Create reliable, fast product systems that are observable and stable for customers.
• Shape product direction through engineering judgment and prioritization.

Skills

Production software

User-facing products

Data-intensive systems

AI/ML products

Frontend

Backend

Systems

Product infra

Job description

Goodfire is seeking Platform Engineers to build the core product experience for training, evaluating, debugging, and deploying interpretable AI systems at scale. You will turn Goodfire's research and platform capabilities into usable products with clear interfaces, dev tools, and surfaces that make model internals understandable.

This role sits at the intersection of research and product, requiring collaboration across engineering, design, research, and field teams to translate state-of-the-art