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Goodfire blog: Fellowship Fall 25
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This announcement is primarily relevant to researchers and engineers seeking fellowship opportunities in mechanistic interpretability; it also signals Goodfire's research priorities and collaborators working on interpretability for AI safety.
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Importance: 30/100blog postnews
Summary
Goodfire is launching a fall 2025 fellowship program bringing on Research Fellows and Research Engineering Fellows to work full-time in San Francisco on core interpretability projects. The 3-month intensive program targets early- to mid-career researchers and engineers, with research directions spanning representational structure, scientific discovery via interpretability, causal analysis, and representation dynamics. Exceptional fellows may convert to full-time positions.
Key Points
- •3-month intensive, in-person fellowship in San Francisco pairing fellows with senior Goodfire researchers on active interpretability projects
- •Research directions include generalization/memorization structure, interpretability for scientific discovery (e.g., genomics), causal analysis, and dynamics of representations
- •Fellows expected to produce tangible outputs: co-authored papers, products, or infrastructure by program end
- •Designed for early- to mid-career ML researchers/engineers; interpretability background not required but adjacent expertise is
- •Opportunity for exceptional fellows to convert to full-time research positions at Goodfire
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Announcing Goodfire’s Fellowship Program for Interpretability Research
Blog
Announcing Goodfire's Fellowship Program for Interpretability Research
Published
October 9, 2025
We're excited to announce that we'll be bringing on several Research Fellows and Research Engineering Fellows this fall for our fellowship program. Fellows will collaborate with senior members of our technical staff, contribute to core projects, and work full time in person in our San Francisco office. For exceptional candidates, there will be an opportunity to convert to full time research positions.
Why we're launching this program
We're launching the fellowship to accelerate interpretability research, which we believe is essential to building aligned, powerful AI models. The fellowship is designed for early- to mid-career researchers and engineers who are interested in the field; we're particularly excited about great engineers transitioning into interpretability research engineering.
We're focused on a number of research directions — e.g., scientific discovery via interpretability on scientific models, training interpretable models, and new interpreter methods — and the program will bring on a few talented researchers to push forward each direction.
What fellows should expect
Every fellow is expected to hit the ground running. The fellowship will be intensive, you'll be expected to learn new methods rapidly, and you will make real contributions to our research. By the end of the 3 months, every fellow will produce a tangible output. This might be a co-authored research paper, a product, or a piece of infrastructure.
By the start of the fellowship, all fellows will be matched with a senior researcher at Goodfire who will be their research collaborator.
Examples of our research directions
Representational structure of generalization/memorization - Jack Merullo
e.g. Could we tell if gpt-oss was memorizing its training data? , Talking Heads
Interpretability for scientific discovery - Dan Balsam , Michael Pearce , Nick Wang
e.g. Finding the Tree of Life in Evo 2 ; see Goodfire Announces Collaboration to Advance Genomic Medicine with AI Interpretability
Causal analysis - Atticus Geiger
e.g. Language Models use Lookbacks to Track Beliefs ; see How Causal Abstraction Underpins Computational Explanation
Dynamics of representations - Ekdeep Singh Lubana
e.g. ICLR: In-Context Learning of Representations , In-context learning strategies emerge rationally
Other directions - Tom McGrath , Owen Lewis
Fellows will receive:
Competitive compensation aligned with experience and qualifications
Full coverage of necessary compute and API costs
Direct mentorship from a Member of Technical Staff
Opportunity to co-author published research in some cases
Who we're looking for
We are looking for talented early- to mid-career researchers or engineers with a strong background in ML who can
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