Staff / Principal Research Engineer, AI Safety Mitigations

About this role

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. At Lila Sciences, you will develop and test safeguards that reduce societal risks from increasingly capable closed loop scientific AI. This is a high-priority individual contributor role for an engineer who has built safety interventions within the development and deployment of frontier models and wants to apply that experience to scientific models, agents, tools and laboratory-connected workflows.

You will work with domain scientists, risk researchers, evaluators and engineering teams to turn risk findings and policy requirements into effective interventions. The work spans research and implementation: developing approaches, testing their limits and helping integrate reliable safeguards into the systems where they are needed. We are open to different levels of seniority and depth across mitigation approaches. What You'll Be Building Translate scientific risk assessments, dual-use policy boundaries and evaluation findings into concrete mitigation designs and testable requirements.

Develop and implement safeguards suited to the problem, potentially spanning model training, classifiers, monitoring, access controls and runtime interventions. Build experiments that measure risk reduction, robustness and effects on legitimate scientific use, alongside practical performance constraints. Use adversarial findings and observed failures to improve safeguards, working closely with automated-safety and scientific evaluation researchers.