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Director, Generative Design & Optimization, Biotherapeutics

Job in San Diego, San Diego County, California, 92189, USA
Listing for: Lilly
Full Time position
Listed on 2026-10-19
Job specializations:
  • Research/Development
    Research Scientist, Biotech Research, Drug Discovery
Salary/Wage Range or Industry Benchmark: 193500 - 338800 USD Yearly USD 193500.00 338800.00 YEAR
Job Description & How to Apply Below

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve.

This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.

Director, Generative Design & Optimization, Biotherapeutic

At Lilly, we unite caring with discovery to help make life better for people around the world. For more than 25 years, Lilly's Biotechnology Discovery Research (BioTDR) organization has advanced novel antibody and peptide therapeutics from concept through clinical development to market in areas of high unmet medical need.

This role is based at the Lilly Biotechnology Center in San Diego, where we are expanding our computational capabilities to build a generative design platform. By bringing together AI/ML, protein engineering, automation, structural biology, and high-throughput experimentation, BioTDR is creating an integrated de novo design platform capable of generating, ranking, and optimizing novel therapeutic antibodies and other biotherapeutics with improved activity, safety, manufacturability, and target coverage.

Designs are rapidly evaluated through experimental validation, creating a continuous learning cycle that strengthens both models and discovery outcomes. The program is supported by dedicated capacity on Lilly Pod, our wholly owned 1,016-GPU NVIDIA Blackwell Ultra SuperPOD.

Help us push the de novo protein design frontier beyond binders to molecules that can become medicines, faster. Ready to make an impact? Join us.

Primary Responsibilities

Our Generative Design platform has three pillars:
Generative Foundation Models ,
Generative Protein Design & Optimization , and Active Learning & Design Validation .

The Director of Generative Design & Optimization will lead the scientific execution of Lilly's de novo biologics design capabilities, transforming foundation-model outputs into experimentally testable therapeutic candidates. You will be accountable for the design workstream end-to-end, spanning design generation, conditioning, sampling, scoring, optimization, and sequence selection. Working closely with the Foundation Models and Active Learning & Design Validation teams, you will help define and execute the technical strategy that advances AI-enabled biotherapeutic discovery.

You will lead a team of AI researchers and computational scientists while remaining sufficiently hands-on to guide technical direction, challenge assumptions, and identify new opportunities. This is an opportunity for a scientific leader who wants to build and scale technologies that directly influence therapeutic molecule discovery.

Responsibilities

  • Conditioning and controllable molecule design. Lead the development of conditioning, guidance, and restraint methods that steer foundation-model output toward epitope-specific, structurally plausible antibody and VHH designs.

  • Sampling and optimization. Devise the iterative optimization layer and sampling strategy that balances exploration and exploitation to maximize design quality, experimental success, and discovery efficiency.

  • Scoring and selection. Lead the development of scoring and ranking methods that combine learned and physics-based signals so the pipeline reliably prioritizes designs based on binding affinity, functional performance, develop ability, and…

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