Advisor - AI-Guided Optimization for Biologics

Lilly • San Diego, CA

8d

About The Position

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.
Lilly’s Biotechnology Discovery Research (BioTDR) organization has a track record of delivering novel biotherapeutic medicines into clinical research across key areas of unmet medical need. By integrating biology with innovative scientific capabilities in protein discovery, engineering, and computational sciences including AI/ML, we are committed to delivering the next wave of biomedicines.
We seek a passionate, creative, and collaborative scientist to join our multidisciplinary team, embedded in BioTDR at our San Diego site. You will contribute to setting the long-term technical vision and research strategy and address the outstanding challenges in biologic design and engineering for therapeutic proteins, antibodies, peptides, and bioconjugates. Working at the intersection of Computational protein design, Protein Bioscience, High-Throughput Experimentation / Lab Automation, Protein Engineering, and Information Technology, you will play a prominent role in advancing our agentic DMTA vision with cross-functional teams, establishing AI-orchestrated pipelines with human oversight to accelerate biotherapeutics discovery. The successful candidate will thrive in a fast-paced environment that emphasizes teamwork, scientific excellence, rigor, and integrity.

Requirements

• Education: Ph.D. in Machine Learning, Computer Science, Computational Biology, Physics, Applied Mathematics, or a closely related quantitative field.
• Experience: 1–3 years of post-Ph.D. experience in industry R&D or a relevant postdoctoral appointment.

Nice To Haves

• Optimization Expertise: Deep theoretical and practical knowledge of Bayesian Optimization, Active Learning, and sequential decision-making under uncertainty.
• RL Fluency: Working knowledge of reinforcement learning (PPO, DPO, RLHF-style alignment, reward shaping) sufficient to design RL and evaluate when RL adds value over simpler methods and to implement RL-based model alignment when warranted.
• Deep Learning Proficiency: Strong hands-on experience with modern deep learning architectures, particularly transformers, diffusion, and flow-based models.
• Software Development: Proficiency in Python and PyTorch (or equivalent frameworks). Experience with distributed training, GPU-accelerated workflows, and writing production-quality code.
• Biologics or Protein Science: Familiarity with protein sequence and structure representation, protein language models (e.g., ESM-family, AbLang), or related biomolecular ML. Experience applying ML to antibody, nanobody, or peptide design is strongly preferred.
• Generative Biologics Experience: Direct experience with generative models for protein or molecular design, including structure-conditioned generation, inverse folding, or de novo antibody design (e.g., Boltz, Chai, RFDiffusion, AF-Multimer).
• RL for Molecular Optimization: Prior work applying RL to molecular property optimization, active learning for drug discovery, or Bayesian optimization in chemical/biological design spaces.
• Scaling Laws: Understanding of data and compute scaling laws for language models and their implications for protein language model training.
• Multi-Modal Models: Experience with multi-modal architectures that jointly model sequence, structure, and functional annotations.
• Scientific Communication: Strong track record of peer-reviewed publications and presentations at leading ML or computational biology venues.
• Open-Source Contributions: Active contributions to open-source ML or computational biology tools and frameworks.

Responsibilities

• Active Learning & Multi-Objective Optimization: Design and establish Active Learning pipelines for multi-objective optimization that balance competing molecular design objectives (affinity, specificity, stability, immunogenicity, manufacturability), including multi-property guidance, Pareto-optimal search strategies, and principled uncertainty quantification.
• Reward & Surrogate Modeling: Design and train reward models and discriminative classifiers (e.g., for affinity ranking, stability prediction, developability scoring) that serve as objective functions for optimization loops.
• Reinforcement Learning for Generative Model Alignment: Develop and implement reinforcement learning strategies (PPO, DPO, reward-weighted approaches) to fine-tune generative models-including autoregressive transformers and diffusion models-toward biologic sequences with desired therapeutic properties. Evaluate when RL-based approach is warranted and when simpler Bayesian Optimization or Active Learning will deliver faster, more reliable results.
• Agentic DMTA Pipelines: Build AI-orchestrated pipelines that connect generative design, property prediction, experiment selection, and result interpretation into a semi-autonomous loop with human oversight. Optimize the workflows to ensure scientific rigor with technically functionality.
• Cross-Functional Leadership: Lead joint data reviews with dry and wet-lab scientists to diagnose and apply learning toward next design cycle. Collaborate with protein engineers, structural biologists, and automation scientists to encode domain knowledge as reward signals, action-space constraints, and optimization boundaries.
• Scientific Communication: 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.

Benefits

• Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance).
• 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).

What This Job Offers

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