AI Scientist (Model Building & Training)

Lilly • San Francisco, CA

10d • $168,000 - $268,400 • Hybrid

About The Position

Where AI Meets Medicine: Build the Future of Drug Discovery in the Heart of Silicon Valley! Making medicine that’s never been made means doing what’s never been done. If you’re an engineer, scientist, or builder who thrives on problems no one has solved before, this is your invitation, we want you on the team. We are ready to challenge the status quo and push medicine forward, all in the name of health. Are you up for the challenge? If so, join us! About the Lilly and NVIDIA Partnership Lilly and NVIDIA are launching a new AI co-innovation lab in the heart of Silicon Valley — an up-to-$1 billion, multi-year commitment to solve drug discovery’s toughest challenges. The lab brings Lilly scientists, technologists, chemists and biologists together with NVIDIA engineers under one roof. Together, we are building purpose-built foundation and frontier AI models trained on Lilly data at scale, tightening the feedback loop between automated wet labs and computational dry labs, designing the next generation of medicines for millions of patients across the globe.

Requirements

• Deep expertise in computational chemistry/biology, genomics, or a related scientific field.
• Recognized expertise in ML research, with significant contributions to generative, predictive, or foundation models for scientific applications.
• Shown impact through publications at leading research venues, patents, open-source contributions, or other notable scientific achievements.
• Advanced proficiency in Python and modern machine learning frameworks (e.g., PyTorch or JAX), with the ability to independently design, implement, train, and evaluate AI models.
• Experience leading complex research initiatives from hypothesis through scientific validation and impact.
• Demonstrated ability to collaborate across scientific and technical disciplines and communicate complex findings to diverse audiences.
• Experience designing, training, and evaluating large neural networks on GPU infrastructure.
• Prior experience applying machine learning to chemical, biological, or other scientific data.
• PhD in Machine Learning, Computer Science, Computational Chemistry, Computational Biology, Bioinformatics, Physics, Statistics, or a closely related quantitative field.
• 2+ years of relevant research experience beyond the PhD, in industry or in a postdoctoral research position.

Responsibilities

• Design, train, and evaluate foundation models that advance scientific discovery across chemistry and biology.
• Conduct fundamental research to develop AI approaches that deepen our understanding of molecular and biological systems.
• Build and validate generative and predictive models that help transform scientific insights into drug discovery breakthroughs.
• Advance the state of the art in AI for drug discovery by designing and developing generative and predictive models for molecular, chemical, and biological systems.
• Translate machine learning into scientific impact across challenges such as molecular design, target identification, genomics, and structure-based discovery.
• Design rigorous evaluation strategies that connect model performance to meaningful scientific and experimental outcomes.
• Partner closely with researchers, engineers, and domain experts to accelerate the application of AI in discovery programs.
• Drive research from concept to impact, balancing scientific innovation with practical application.

Benefits

• company bonus (depending, in part, on company and individual performance)
• company-sponsored 401(k)
• pension
• vacation benefits
• medical, dental, vision and prescription drug benefits
• flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts)
• life insurance and death benefits
• certain time off and leave of absence benefits
• well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities)

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree