Mentees will map the safeguard landscape for biological AI models and evaluate which mitigations hold up. Work spans surveying protein and genomic design models, characterizing existing mitigations across the model lifecycle, and assessing where they fail.
About the project
Specialized biological AI models for protein and genomic design are proliferating faster than the safeguards meant to prevent their misuse. This project engages with key international stakeholders to evaluate concrete mitigations for these models across their lifecycle, from training through deployment.
Theory of change
LLM developers have converged on a rough set of biological safeguard norms, driven partly by external evaluation of the kind SecureBio does. No equivalent convergence exists for biological design models, and the window in which norms can be set cheaply is open now, before capability and proliferation close it. What international standard-setting processes currently lack is a clear, evidence-based account of which mitigations exist and which ones actually work. SecureBio's existing evaluation work on biological AI models, and our positions with NIST and the European AI Office, give this a direct route into those processes.
Your role
The mentee will lead the landscape and evaluation work described above, with the project lead setting scope and reviewing. Depending on how the project develops, they may be included in conversations with external stakeholders.
Prerequisites
Able to read and accurately summarize machine learning papers on protein or genomic models. A computational biology or protein ML background is the most direct fit. Strong technical writing. Interest in the governance and standards side, not only the technical side. Python is useful but not required.
Application question(s)
Please answer one of the below, 300-500 words.
- How should a managed-access program for bio-capable models be set up? What are the relevant parameters, what do you suggest, and why?
- Suppose a wet-lab uplift study is run. What kind of data would you want to collect and how would you propose those data inform subsequent in silico model evaluations?
About the mentor
SecureBio is a nonprofit biosecurity research organization specializing in technical research to mitigate risks from catastrophic pandemics. Our AI team develops rigorous benchmarks and evaluation frameworks to assess AI systems' biological capabilities, as well as mitigation strategies that can reduce risks once AI capabilities cross specific risk thresholds. We perform pre-release safety testing of frontier models (e.g. GPT-5.6), and our evaluations have been featured in the model cards of OpenAI, Anthropic, and Google DeepMind. Our work has also informed national security briefings and emerging governance standards.