SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 268,000 - 384,000 / annual
Lila Sciences is building Scientific Superintelligence to solve major scientific challenges by combining advanced AI models with proprietary AI Science Factory instruments. The company is assembling a talent-dense AI safety team to identify and mitigate risks from scientific superintelligence while preserving core scientific discovery capabilities.
In this role, you will conduct original computational biology research focused on 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. Your work will span protein and nucleic-acid design, cellular systems, and biological delivery, with openness to other specialisms that bring relevant scientific depth and risk judgment.
Key responsibilities include:
- Formulate and pursue original computational biology research questions relevant to safety, developing computational approaches and designing 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 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.
- 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 studies, test assumptions, and assess safeguards.
Requirements:
- Deep expertise (PhD or comparable experience) in computational biology or a related quantitative biological discipline, with a record of original research from question formulation and computational study design through to defensible findings.
- Substantive experience assessing biological dual-use risks, with a strong understanding of relevant harmful applications, including pathogen- or toxin-related risks or other consequential biological misuse.
- Prior experience applying biological expertise to AI safety through analysis informing threat models, evaluation or red-teaming work, or mitigation design.
- Sound judgment about the relationship between computational predictions, biological function, and experimental evidence, including uncertainty and the limits of your expertise.
- Strong quantitative analysis and scientific programming skills, with the ability to develop computational approaches, run reproducible studies, and critically investigate model outputs and biological data.
- The ability to investigate unfamiliar questions, identify appropriate specialists, and communicate defensible conclusions across scientific, engineering, and policy teams.
Bonus qualifications:
- Experience connecting biological modeling or design to experimental validation, including workflows combining LLMs, specialized models, agents, or laboratory automation.
- Experience commissioning specialist research or coordinating external scientific collaborators.
