Full job description

Your Impact at LILA

Lila is developing AI systems for scientific discovery, connecting advanced models with scientific tools and automated laboratories. As these systems become more capable, we need to understand how they could be misused or compromised, which protections matter most, and how to establish that those protections work together.

We're hiring an experienced security practitioner to lead this work at the boundary between AI safety and security. You will investigate real systems, develop threat models, test important assumptions and recommend the protections needed for particular capabilities and deployments. Your work will address both dangerous misuse and the protection of sensitive models, data and research.

This is an opportunity to help shape a developing area of AI safety through security practice. You will combine technical investigation with careful analysis and clear writing, working closely with our established Security team, researchers and engineers to turn findings into implemented improvements.

You will bring security depth and develop your understanding of model capabilities, evaluations and safeguards through that work. Prior experience in AI safety is welcome, but not required.

What You'll Be Building

Threat-model consequential systems and capabilities. Investigate how dangerous misuse or compromise could occur across models, deployed services and scientific workflows. Identify the relevant actors, access routes and failure paths, and the protections that could interrupt them.
Test the assumptions behind our defenses. Examine architectures, code, configurations and operational evidence. Run focused adversarial tests, build prototypes and investigate weaknesses, including situations where several protections depend on the same underlying system.
Shape the protections needed for safe deployment. Turn dangerous-capability assessments into concrete security requirements. Assess how compromise of models, sensitive assets or supporting systems changes the safety assessment, drawing on personnel, physical and other security specialists where relevant.
Make evidence-based recommendations. Define what evidence would demonstrate that important controls and safeguards work together. Combine findings from model evaluations and security testing, explain the remaining uncertainty, and recommend improvements or conditions for use.
Carry findings through to practical improvements. Work with Security, Safety and engineering owners to prioritize changes, specify acceptance criteria and verify the results. Make clear recommendations about which gaps require attention first and why.