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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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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_](https://scholar.google.com/citations?user=7w0xLF4AAAAJ&hl=en)
e.g. [Could we tell if gpt-oss was memorizing its training data?](https://x.com/jack_merullo_/status/1953860284638278043), [Talking Heads](https://arxiv.org/abs/2406.09519)
**Interpretability for scientific discovery** \- [_Dan Balsam_](https://www.linkedin.com/in/dbalsam/), [_Michael Pearce_](https://www.linkedin.com/in/michaelttpearce/), [_Nick Wang_](https://www.linkedin.com/in/nkwang24/)
e.g. [Finding the Tree of Life in Evo 2](https://www.goodfire.ai/research/phylogeny-manifold); see [Goodfire Announces Collaboration to Advance Genomic Medicine with AI Interpretability](https://www.goodfire.ai/blog/mayo-clinic-collaboration)
**Causal analysis** \- [_Atticus Geiger_](https://atticusg.github.io/)
e.g. [Language Models use Lookbacks to Track Beliefs](https://arxiv.org/abs/2505.14685); see [How Causal Abstraction Underpins Computational Explanation](https://arxiv.org/abs/2508.11214)
**Dynamics of representations** \- [_Ekdeep Singh Lubana_](https://ekdeepslubana.github.io/)
e.g. [ICLR
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