Reinforcement learning for generative model alignment
Ai-orchestrated dmta pipelines
Contribute to setting the long-term technical vision and research strategy for biologic design and engineering
Job Summary
Contribute to setting the long-term technical vision and research strategy for biologic design and engineering.
Build AI-orchestrated pipelines that connect generative design, property prediction, experiment selection, and result interpretation into a semi-autonomous loop with human oversight.
Publish research findings in top-tier venues, present at internal and external conferences, and contribute to Lilly’s external scientific reputation in AI-driven drug discovery.
Matching Summary
Contribute to setting the long-term technical vision and research strategy for biologic design and engineering.
Salary
Base: $166,500 - $244,200; Bonus/Equity: Company bonus; Benefits: Comprehensive benefit program including 401(k), pension, vacation, medical, dental, vision, life insurance, and well-being benefits
Skills & Requirements
Must-have
Active Learning for multi-objective optimization
Reinforcement learning for generative model alignment
AI-orchestrated DMTA pipelines
Deep learning architectures (transformers, diffusion)
Python and PyTorch proficiency
Nice-to-have
Protein sequence and structure representation
Protein language models
Generative models for protein design
Open-source ML contributions
Multi-modal model experience
Key Requirements
Ph.D. in Machine Learning, Computer Science, Computational Biology, Physics, Applied Mathematics, or related quantitative field
1-3 years of post-Ph.D. experience in industry R&D or relevant postdoctoral appointment