Scientist/Sr. Scientist, AI Safety

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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Mental health support

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Relocation assistance

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Childcare support

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Learning budget

Offered From the posting source checked Jun 20, 2026

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Salary

$228K-$358K From the posting source checked Jun 20, 2026

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Role

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

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Weekend work

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Company

Company stage

Early From the posting source checked Jun 20, 2026

Equity

Offered From the posting source checked Jun 20, 2026

Application

Cover letter

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Assessment

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Deadline

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Where they hire

State eligibility is not yet verified.

About this role

Scientist/Sr. Scientist, AI Safety Cambridge, MA USA; London, UK; San Francisco, CA USA 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 prepare for risks from scientific superintelligence. 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, as well as developing technical collateral including risk- and capability-focused evaluations and safeguards. What You'll Be Building - Evaluations to test for scientific risks (both known but especially novel) from cutting edge scientific models integrated with automated physical labs - Initial proof-of-concept safeguards, such as ML models to detect and block unsafe behavior from scientific AI models, as well as from physical lab outputs. - Understanding of a range of model capabilities, across primarily scientific but also non-scientific domains (e.g. persuasion, deception) to inform Lila's broader safety strategy. - Broader, high-quality research efforts - as and when needed - for scientific capability evaluation and restriction. What You'll Need to Succeed - Bachelor's degree in a technical field (e.g., computer science, engineering, machine learning, mathematics, physics, statistics), or related experience. - Strong programming skills in Python, and experience with ML frameworks (including, for instance, Inspect) for large-scale evaluation and scaffolded testing. - Experience in building evaluations, or conducting red-teaming exercises, for CBRN / cyber risks (or

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