Organization OverviewLilly Catalyze360 is a comprehensive approach to enabling the early-stage biotech ecosystem by democratizing access to infrastructure, expertise, and resources. Through its interconnected pillars-Lilly Ventures, Lilly Gateway Labs, Lilly ExploR&D, and Lilly TuneLab-Catalyze360 strategically removes barriers that traditionally block bold science from becoming life-changing medicines, providing biotechs with flexible combinations of capital, physical lab space, R&D capabilities, AI/ML tools, and decades of enterprise learning.
Lilly TuneLab is an artificial intelligence and machine learning (AI/ML) platform that provides biotech companies access to drug discovery models trained on years of Lilly's research data. Lilly estimates that this first release of AI models includes proprietary data obtained at a cost of over $1 billion, representing one of the industry's most valuable datasets used to train an AI system available to biotechnology companies. By integrating advanced in silico modelling and federated learning, we connect pioneering machine learning algorithms, substantial computational power, exclusive datasets, and Lilly's domain-specific knowledge to drive innovation in drug discovery and facilitate access to optimal therapies for patients.
Job Summary 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.
Key Responsibilities- 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.
- Downstream Adaptation & Validation: 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.
- Small Molecule Property Prediction: 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).
- Generative Chemistry Models: 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.
- ADMET-Driven, Multi-Objective Design: 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.
- Chemical Space & Synthetic Feasibility: 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.
- Structure-Activity & Representation Learning: 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.
- Interpretability & Scientific Insight: 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.
- Benchmarking, Dissemination & Governance: 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.
Basic Qualifications- 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)
Additional Preferences- 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
Other Information- This role is based at Lilly sites in Indianapolis, San Francisco, or Boston with up to 10% travel (attendance expected at key industry conferences).
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$177,000 - $281,600
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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