Hiring: Director, Molecular AI & Federated Learning Company: Eli Lilly and Company Location: Indianapolis, IN Job Posted Time: 2026-09-10 11:02:26 Employment Type: Full-time Target Skills & Keywords : AI, Accessibility, Deep Learning, Diffusion Models, Fine-tuning, Machine Learning, PyTorch, R About the job Experience: •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) Required Skills: •Technical Vision & Research Strategy: 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. •Technical Leadership & Mentorship: 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. •Federated Foundation Models: 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. •Semi-Supervised & Self-Supervised Learning: 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. •Federated Optimization & Aggregation: 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. •Scalability, Simulation & Performance: 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. •Federated Multi-Task Learning: 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. •Data & Task Heterogeneity: 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. Qualifications: •PhD in Computer Science, Computational Chemistry, Cheminformatics, Machine Learning, Computational Biology, or a related computational field from an accredited college or university •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 •Comprehensive expertise in medicinal chemistry principles and ADMET optimization •Applied hands-on capability in 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 •Knowledge of PK/PD modeling and clinical translation •Proficiency in cheminformatics tools (RDKit, DeepChem) and modern ML frameworks (e.g., PyTorch) Compensation: •$177,000 - $281,600 / year •Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance) Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!