Senior / Principal Computational Biologist, AI Safety

Lila Sciences • Cambridge, MA

6d • $268,000 - $384,000

About The Position

Lila Sciences is building a high-agency AI safety team to engage with the organization’s science, model training, and lab integration teams. The team will identify and mitigate risks from scientific superintelligence while retaining the scientific discovery capabilities of its systems. Its work includes developing a safety strategy tailored to Lila’s goals and deployment plans, engaging with the broader ecosystem, conducting safety-focused evaluations, building risk-mitigation systems, and researching longer-term needs such as oversight of superintelligent scientific systems.

The Computational Biologist will conduct original research relevant to AI safety and build an integrated understanding of biological risks. The role examines how large language models, specialized biological models, agents, and automated laboratories interact to change what is biologically feasible. Areas of interest include protein and nucleic-acid design, cellular systems, and biological delivery, as well as other specialisms that provide relevant scientific depth and risk judgment. The work translates scientific risk assessments into policy boundaries, evaluation priorities, and mitigation requirements while preserving beneficial biological research.

Requirements

• Deep expertise in computational biology or a related quantitative biological discipline, demonstrated by a PhD or comparable experience.
• A record of original research, from formulating questions and designing computational studies through reaching defensible findings.
• Substantive experience assessing biological dual-use risks and 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 that informs 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 one’s expertise.
• Strong quantitative analysis and scientific programming skills, including the ability to develop computational approaches, run reproducible studies, and critically investigate model outputs and biological data.
• Ability to investigate unfamiliar questions, identify appropriate specialists, and communicate defensible conclusions across scientific, engineering, and policy teams.

Nice To Haves

• 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.

Responsibilities

• Formulate and pursue original computational biology research questions relevant to safety, developing computational approaches and designing and running studies that produce evidence about biological capabilities, risks, and safeguards.
• Build and update an integrated picture of biological risks, examining how LLMs, specialized biological models, agents, and automated laboratories interact in 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 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 evaluations and red-teaming studies, test assumptions, and assess safeguards.

Benefits

• Competitive base compensation with bonus potential and generous early-stage equity.
• For full-time U.S. employees: medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off; generous company-wide holidays; paid parental leave; educational assistance; commuter benefits, including bike-share memberships for office-based employees; and a company-subsidized lunch program.
• For full-time employees outside the U.S.: a comprehensive benefits program tailored to their region.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree