Director / Senior Director, AI for Life Sciences
Lila SciencesSan Francisco, CA USAPosted 26 March 2026
Tech Stack
Job Description
Your Impact at Lila
Lila is embarking on a transformative mission to redefine the future of medicine by combining automated large-scale data generation with scientific superintelligence. At Lila, we don't just use AI to analyze biology; we are building the loop where AI and automation co-evolve to solve the hardest problems in medicine.
To this end, we are forming a new function, Life Science AI (LSAI), with the goal of researching automated reasoning on biological data by combining state-of-the-art machine learning with breakthrough biology.
As a member of the LSAI leadership team, you will be a founding architect of this vision. You will play a critical role in shaping the culture, technical strategy, and therapeutic impact of AI at Lila. This is a high-impact leadership role reporting directly to the SVP of Life Science AI. Depending on the candidate’s depth of expertise, this role may focus on a single domain or span multiple pillars to drive platform-wide AI strategies.
What You'll Be Building
Team Leadership Scientific Strategy: Recruit, mentor, and lead a high-performing team of ML researchers and computational biologists. Foster a culture of "bilingual" excellence that thrives at the intersection of AI and biology.
Cross-Functional Synergy: Act as the primary bridge to our Life Science Experimental Research team. You will ensure computational designs are optimized for "wet-lab" validation and architect the active learning feedback loops that allow experimental data to continuously improve our generative models.
Product Integration: Partner with Life Science Product Management to align technical innovations with Lila’s commercial goals, ensuring our AI capabilities translate directly into therapeutic priorities.
Foundational Culture: As a founding member of LSAI, you will establish the standards for engineering excellence, reproducible research, and collaborative innovation across the organization.
What You’ll Need to Succeed
Areas of Impact: Successful candidates will demonstrate deep expertise in one or more of the following areas. The final scope of the role will be tailored to the leader’s unique background:
Protein Design: De novo protein scaffolds, binders, therapeutic antibodies, and enzyme engineering.
Nucleic Acid Engineering: DNA/RNA vaccines, regulatory elements (UTRs, promoters), and viral vector optimization.
Cell Biology: Foundation models for -omics (e.g., scRNAseq), perturbation data, and spatial profiling to drive target identification and mechanism-of-action studies.
Education: PhD with a focus on computation, specifically in Computational Biology**,** Computer Science**,** or AI-driven drug discovery.
Technical Track Record: Publications in top AI/ML Scientific conferences (NeurIPS, ICML, ICLR, ACL Nature, Science, Cell Press Matter, IOP).
Leadership: Demonstrated experience building and leading multidisciplinary teams and managing complex research portfolios in a fast-paced environment.
Communication: Exceptional skills in translating high-dimensional ML concepts for biological scientists and presenting strategic visions to executive stakeholders.
Bonus Points For
Location: This role is based in San Francisco, CA or Boston, MA, with a strong preference for candidates located in or willing to relocate to San Francisco.
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 lis ... (truncated, view full listing at source)
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