Director, Molecular AI & Federated Learning

Eli Lilly and Company • San Francisco, IN

10d • Hybrid

About The Position

The Director, Molecular AI & Federated Learning is a senior technical leadership role within the TuneLab platform, setting the technical vision that unites privacy-preserving federated learning with generative small-molecule design. This position pairs deep expertise in medicinal chemistry, ADMET prediction, and molecular optimization with advanced capabilities in federated foundation models and multi-task learning, and is responsible for the predictive and generative models that accelerate small-molecule lead optimization and candidate selection across the TuneLab federated network. As a technical director, the role leads through vision, methodological rigor, and mentorship—guiding scientists and shaping research strategy across internal teams and external biotech partners—rather than through formal people management.

Requirements

• PhD in Computer Science, Computational Chemistry, Cheminformatics, Machine Learning, Computational Biology, or a related computational field from an accredited college or university
• 5+ years of post PhD experience applying machine learning to drug discovery within the biopharmaceutical industry or comparable settings or an equivalent record of technical leadership and impact (preference for 8+ years)
• Demonstrated technical leadership—setting research direction, leading complex ML programs, and mentoring scientists—without a requirement for formal people-management experience
• Proven track record developing generative models for molecular design and multi-task or representation-learning models for complex endpoints
• Deep understanding of medicinal chemistry principles and ADMET optimization
• Hands-on experience with federated learning, distributed optimization, and privacy-preserving machine learning
• Publications in top-tier venues (e.g., NeurIPS, ICML, ICLR) on molecular generation, property prediction, or federated and representation learning
• Expertise in graph neural networks and geometric deep learning for molecules
• Strong background in organic chemistry and synthetic-feasibility assessment
• Experience with fragment-based and structure-based drug design
• Knowledge of PK/PD modeling and clinical translation
• Proficiency in cheminformatics tools (RDKit, DeepChem) and modern ML frameworks (e.g., PyTorch)
• Experience with active learning and design–make–test–analyze cycles
• Familiarity with uncertainty quantification and explainability (XAI) in federated or multi-task settings
• Exceptional communication skills, with the ability to understand and navigate complex relationships across disciplines, internally and externally
• Learning agility and a portfolio mindset—ensuring individual technical decisions align with the overall goals of the TuneLab ecosystem
• Independent, self-directed, and able to drive ambiguous research problems through to impact

Responsibilities

• Set the technical direction for federated learning and molecular AI across TuneLab—defining a research agenda that unifies privacy-preserving foundation models, multi-task learning, and generative small-molecule design, and aligning it with platform and portfolio priorities.
• Serve as a principal technical authority and mentor for data scientists and engineers—guiding experimental design, reviewing methods and code, and raising the scientific bar across the team, while influencing technical decisions across disciplines internally and with external partners.
• Architect novel deep learning architectures (e.g., Transformer and graph neural network–based) for large-scale federated pre-training on unlabeled or partially labeled data distributed across multiple partner sources.
• Advance state-of-the-art semi-supervised and self-supervised methods (e.g., contrastive learning, masked auto-encoding) tailored to the constraints of federated learning, such as communication bottlenecks and data heterogeneity.
• Develop robust, communication-efficient aggregation strategies (e.g., FedAvg, FedProx, SCAFFOLD) that remain stable for large, complex models and handle non-IID data across clients.
• Profile and optimize the computational performance—memory, latency, and communication cost—of federated training and inference for scale, and build high-fidelity simulation environments to test, debug, and benchmark federated strategies before real-world deployment.
• Architect multi-task learning models that leverage shared representations across related endpoints to improve predictive performance and data efficiency in a federated ecosystem, where each client may hold data for only a subset of tasks.
• Design algorithms that address extreme task and feature heterogeneity across clients—personalized models, meta-learning, and gradient-aggregation methods robust to non-IID data—and apply regularization that prevents negative transfer while encouraging positive knowledge sharing.
• Create efficient protocols for fine-tuning and adapting pre-trained federated models to specific downstream tasks, and establish rigorous validation frameworks with appropriate per-task metrics and fairness assessment across clients and tasks.
• Build multi-task models for small-molecule properties—including ADMET endpoints, solubility, permeability, metabolic stability, and off-target liabilities—across diverse chemical representations (SMILES, graphs, 3D conformations).
• Design and deploy state-of-the-art generative models (VAEs, diffusion models, flow matching, autoregressive models) for de novo design, lead optimization, and scaffold hopping that respect synthetic accessibility and drug-likeness constraints.
• Develop integrated prediction–generation pipelines that optimize molecules simultaneously across multiple ADMET properties while maintaining target potency, using multi-objective optimization and Pareto-front exploration.
• Implement efficient exploration of synthetically accessible chemical space—reaction-aware generation, retrosynthetic-planning integration, and fragment-based design—collaborating with synthetic chemists to ensure generated molecules are practically synthesizable.
• Learn and exploit structure–activity relationships from sparse, noisy federated bioactivity data—including matched molecular pair analysis and activity-cliff prediction—and develop self- and semi-supervised molecular representations that generalize to novel chemical series.
• Apply explainability (XAI) techniques to complex multi-task and molecular models to understand predictions and uncover relationships between endpoints, generating novel scientific insight while respecting IP and competitive boundaries across federated partners.
• Establish rigorous benchmarks using public (ChEMBL, ZINC, PubChem) and proprietary Lilly data; author high-impact publications (e.g., NeurIPS, ICML, ICLR) and deliver compelling presentations to internal and external audiences; and uphold reproducible code, internal libraries, and version control for data, code, and models.

Benefits

• company bonus (depending, in part, on company and individual performance)
• 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
• 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