Scientist/Sr. Scientist, AI Safety

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 for frontier model capabilities more generally, including both unsafe and benign capabilities)
• Experience in designing and/or implementing (directly or through consultation) AI safety frameworks for frontier AI companies.
• Ability to communicate complex technical concepts and concerns to non-expert audiences effectively.

Bonus Points For

• Masters or PhD in a field relevant to safety evaluations of AI models in scientific domains, or a technical field.
• Publications in AI safety / evaluations / model behaviour in top ML / AI conferences (NeurIPS, ICML, ICLR, ACL) or model release system cards.
• Experience researching risks from novel science (e.g. biosecurity, computational biology, etc.) or working with narrow scientific tools (e.g. large scale foundation models for science).

• This position may be based in any of Lila's offices, including Cambridge (MA), San Francisco (CA), or London (UK).

About Lila

Lila Sciences is the world’s first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science.  We are pioneering a new age of boundless discovery by building the capabilities to apply AI to every aspect of the scientific method.  We are introducing scientific superintelligence to solve humankind's greatest challenges, enabling scientists to bring forth solutions in human health, climate, and sustainability at a pace and scale never experienced before. Learn more about this mission at  www.lila.ai

If this sounds like an environment you’d love to work in, even if you only have some of the experience listed below, we encourage you to apply.

We’re All In

Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

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A Note to Agencies

Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.