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.

About the role

We're looking for a Forward Deployed Engineer to be the technical backbone of our customer engagements, the person who takes our interpretability platform and makes it work inside a partner's environment, end-to-end. This role demands strong engineering capability, the ability to learn new domains fast, the creativity to design solutions under real-world constraints, and the comfort to operate directly alongside customer engineering and research teams.

This is a hands-on, high-ownership role where you will be forward-deployed with our most strategic partners: writing production code, building integrations, running pilots, and owning technical delivery day-to-day.

This role turns frontier research into deployed reality. You will typically be working with deeply technical partners - Heads of AI, research teams, ML platform teams - across Life Sciences, Robotics and Vision, Language and Reasoning models, or new verticals, helping them integrate our platform into their model training stack.

Key responsibilities

Own technical delivery for partner engagements: build production-grade integrations of Goodfire's platform into customer environments — from API integrations and custom pipelines to internal tooling and evaluation workflows
Run pilots that prove value fast: design and execute creative, short-timeline pilots that demonstrate the platform's impact, often in parallel with or ahead of longer-term solution scoping
Embed with partner engineering teams: serve as the day-to-day technical point of contact, going deep in their codebase, understanding their infrastructure, and building trust through competence
Compound learnings across engagements: bring field insights back to the platform and research teams, identify repeatable patterns, and build shared tooling and playbooks that scale the Field Team
Bridge research and production: take novel interpretability methods from the Foundational Team and figure out how to make them work reliably in a partner's stack — this means being comfortable reading papers, prototyping new techniques, and hardening them for production use

What you’ll bring

Required experience

Strong software engineering skills with production experience in Python and modern ML frameworks (PyTorch, JAX).
Ability to work across research and engineering boundaries - you can understand a new technique and figure out how to ship it.
Scrappiness, willingness to persist in ambiguity, ability to learn quickly, and a generalist "can-do" mindset.
Strong communication skills - you can explain technical tradeoffs clearly to both engineers and non-technical stakeholders.

Preferred qualifications

Technical degree or equivalent professional experience within AI, ML, or a related field.
Experience in a forward-deployed, solutions, or customer-facing engineering role. Former technical founders encouraged to apply.
Track record of excellence in a high-growth startup or frontier AI lab.
Experience with large language models, including fine-tuning, evaluation frameworks, or agent development.
Familiarity with interpretability, mechanistic interpretability, or model internals (sparse autoencoders, feature steering, etc.).

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.