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, developability, and other therapeutically relevant properties.
Training corpus strategy. Establish versioned training, fine-tuning, and evaluation corpora with documented provenance, structure-aware splits, composition analysis, and leakage controls. Leverage multimodal and proprietary biological data to create a differentiated advantage in molecular design.
Build and scale integrated design pipelines. Develop and operationalize workflows spanning conditioned generation, sampling, optimization, scoring, diversity selection, and experimental test nomination that can be applied repeatedly across therapeutic discovery programs.
Set performance evaluation. Establish metrics that connect computational design quality to experimental outcomes, therapeutic objectives, and downstream portfolio impact.
Lead a high-performing team and provide scientific leadership. Set technical direction and priorities, recruit and develop scientists and engineers, establish technical standards, and deliver against committed milestones.
Collaboration. Guide the team and work hand in hand with the Foundation Models and Active Learning & Design Validation teams to define how each pipeline component is benchmarked and tested against experimental evidence and translate those results into the next iteration.
Evaluate and incorporate external innovation. Assess advances in generative biology, molecular optimization, active learning, and AI-enabled therapeutic design, determining when to build, adapt, or partner.
Communicate scientific strategy and impact. Present technical progress, experimental validation, key learnings, and strategic recommendations to scientific leadership and broader R&D stakeholders. Contribute to publications, external collaborations, and scientific presentations where appropriate.
Basic Requirements
Ph.D. in computer science, computational biology, bioengineering, chemistry, physics, or a related STEM discipline with 3+years of relevant experience; or an M.S. with 6+years; or a B.S. with 8+ years.
Track record leading computational or generative molecular design efforts that resulted in experimentally evaluated molecular designs.
Expertise in designing and building training or evaluation datasets for sequence, structure, or molecular design models.
Strong Python skills and experience with a modern deep learning framework and GPU computing.
Additional Preferences
Proven record in conditional generation, guided sampling, active learning, Bayesian optimization, multi-objective optimization, quality-diversity methods, or related molecular design approaches.
Expertise in protein structure analysis and translating structural knowledge into model constraints, optimization objectives, or selection criteria.
Expertise in sequence-identity-aware and structure-aware data splitting, benchmarking, and leakage assessment.
Contribution to a de novo binder or antibody design effort that reached experimentally validated designs.
Experience combining learned and physics-based signals in scoring, ranking, or optimization workflows for molecular designs.
Experience mentoring scientists or engineers or leading a multi-person technical effort.
Track record of productive collaboration in multidisciplinary teams spanning computational and experimental sciences.
Ability to communicate across domains and explain design capabilities, opportunities, and limitations to non-computational colleague
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL).
- Actual compensation will depend on a candidate’s education, experience, skills, and geographic location. The anticipated wage for this position is
- $193,500 - $308,000
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for 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; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly’s compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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