Senior / Principal Computational Biologist, AI Safety

Job description

Your Impact at LILA We're building a talent-dense, high-agency AI safety team at Lila that will engage all core teams within the organization (science, model training, lab integration, etc.), to identify and mitigate risks from scientific superintelligence while retaining the core scientific discovery capabilities of the stack. The initial focus of this team will be to build and implement a bespoke safety strategy for Lila, tailored to its specific goals and deployment strategies. This will involve technical safety strategy development, broader ecosystem engagement, safety-focused evaluations, safety systems to mitigate risks, and a research agenda that explores longer-term needs such as oversight of superintelligent scientific systems. You will conduct original computational biology research for AI safety and build an integrated understanding of biological risks. You will investigate how capabilities across large language models, specialized biological models, agents and automated laboratories combine to change what is biologically feasible. Our work spans protein and nucleic-acid design, cellular systems and biological delivery; we welcome other specialisms that bring relevant scientific depth and risk judgment. What You'll Be Building Formulate and pursue original computational biology research questions relevant to safety, developing computational approaches and designing and running studies that generate new evidence about biological capabilities, risks and safeguards. Build and update an integrated picture of risks, examining how capabilities across LLMs, specialized biological models, agents and automated laboratories interact within scientific workflows and change practical constraints. Combine your research with literature, model assessments, experimental findings and expert input to judge biological feasibility, uncertainty and the significance of emerging capabilities for misuse. Identify gaps in expertise, frame questions for external specialists and critically integrate their findings with support from the expert-program team. Translate scientific risk judgments into dual-use policy boundaries, evaluation priorities and mitigation requirements that preserve beneficial biological research. Work with threat modelers, automated-safety researchers, experimental scientists and mitigation engineers to design informative evaluations and red-teaming st…