You will continue developing methods to understand what our foundation model learns about biology, and build the tools that make it a glass box model. We believe that in biology, a model's reasoning must be visible. And the features you find are not just explanations: they expand what the model can do. - You will continue developing our methods for probing and reverse-engineering the model's learned representations, understanding how it encodes biological information across molecular scales - You will design and run experiments to identify and characterize capabilities, mapping what the model has learned about molecular interactions and biological function - You will build methods to extract the model's biological understanding as explicit, usable outputs that downstream systems and researchers can act on - You will create tools that connect model internals to meaningful biological concepts, making the model's reasoning interpretable to scientists and useful in practice - You will work closely with the pretraining and generation teams, feeding interpretability findings back into model development to strengthen the capabilities you uncover - You will own the full pipeline from probing experiments to production-quality interpretability tools, building robust systems on distributed infrastructure About You - You have a PhD in computer science, machine learning, physics, mathematics, or a related field with 2+ years of post-doctoral or industry research experience, or a Bachelor's or Master's degree with 5+ years of hands-on research and engineering experience in model interpretability or representation analysis - You have a strong publication record at top-tier venues (e.g., NeurIPS, ICML, ICLR) with contributions to mechanistic interpretability, representation analysis, probing methods, or model understanding - You have hands-on experience analyzing the internal representations of large neural networks, with demonstrated ability to design experiments that reveal what models have learned - You are proficient in Python and PyTorch, and have experience working with large models on GPU infrastructure - You have demonstrated the ability to take interpretability research from experiments to usable tools: you do not just analyze models, you build systems others can use - You write production-quality code that is well-tested and maintainable, and you are comfortable working in shared codebases with version control and code review - You think carefully about what constitutes evidence that a model has learned a concept, and you design experiments that distinguish real capabilities from artifacts Bonus Points - You have a background in chemistry, biology, computational biology, biophysics, or a related natural science - You have experience interpreting ML models trained on scientific or biological data - You have experience building visualization or analysis tools for model internals - You have experience with multimodal models or representations that span multiple data types - You have contributed to open-source machine learning projects What We Offer - We encourage new and different ideas, creativity and contrarian thinking - Healthy feedback focused environment to help you strive - leadership will have high expectations, regularly share constructive feedback, support you and help you grow, and welcome receiving feedback and ideas from you - You own your day-to-day management. What we care about is that we all hit our milestones - Competitive salary and equity in a growing, well-funded startup - Excellent medical, dental, and vision coverage