Director, Molecular AI & Federated Learning
Eli Lilly and Company
September 03, 2026
On-site
Boston, MA
IT
Job Summary
Director, Molecular AI & Federated Learning (TuneLab) — Senior technical leadership role setting the technical vision for privacy-preserving federated learning and generative small-molecule design; leads through vision, rigor, and mentorship (not formal people management).
Key Responsibilities
- Set technical direction and research agenda for federated molecular AI (foundation models, multi-task learning, generative small-molecule design).
- Mentor and serve as principal technical authority; guide experimental design and review methods/code.
- Architect federated foundation models (e.g., Transformers, graph neural networks) for pre-training across distributed partner data.
- Advance semi-/self-supervised learning suited to federated constraints (communication bottlenecks, data heterogeneity).
- Develop communication-efficient federated optimization/aggregation (FedAvg, FedProx, SCAFFOLD) for non-IID data.
- Profile/optimize scalability (memory, latency, communication cost) and build simulation environments.
- Architect federated multi-task learning and handle task/feature heterogeneity (personalized models, meta-learning, gradient aggregation; prevent negative transfer).
- Create protocols for downstream adaptation/validation with per-task metrics and fairness assessment.
- Build multi-task small-molecule property prediction (ADMET, solubility, permeability, metabolic stability, off-target liabilities).
- Design/deploy generative chemistry models (VAEs, diffusion, flow matching, autoregressive) for de novo design, lead optimization, scaffold hopping.
- Implement integrated ADMET-driven, multi-objective design (Pareto-front exploration) and synthetic feasibility exploration.
- Apply interpretability/XAI and establish rigorous benchmarking and reproducible governance (public + proprietary data; publications/presentations).
Basic Qualifications
- PhD in CS, Computational Chemistry, Cheminformatics, ML, Computational Biology, or related field.
- 5+ years post-PhD ML experience in drug discovery (preference for 8+ years) or equivalent technical leadership/impact.
Additional Preferences
- Technical leadership and mentoring without formal people-management.
- Generative molecular design; multi-task/representation-learning track record; medicinal chemistry + ADMET optimization.
- Hands-on federated learning/distributed optimization/privacy-preserving ML; top-tier publications.
- Graph/geometric deep learning; organic chemistry + synthetic feasibility; fragment/structure-based design.
- PK/PD modeling and clinical translation; RDKit/DeepChem and PyTorch; active learning and D-M-T-A cycles.
- Uncertainty quantification and XAI in federated/multi-task settings; exceptional cross-disciplinary communication; independent, self-directed.
Other Information
- Location: Indianapolis, San Francisco, or Boston; up to 10% travel; attendance expected at key conferences.
Director, Molecular AI & Federated Learning (TuneLab) — Senior technical leadership role setting the technical vision for privacy-preserving federated learning and generative small-molecule design; leads through vision, rigor, and mentorship (not formal people management).
Key Responsibilities
- Set technical direction and research agenda for federated molecular AI (foundation models, multi-task learning, generative small-molecule design).
- Mentor and serve as principal technical authority; guide experimental design and review methods/code.
- Architect federated foundation models (e.g., Transformers, graph neural networks) for pre-training across distributed partner data.
- Advance semi-/self-supervised learning suited to federated constraints (communication bottlenecks, data heterogeneity).
- Develop communication-efficient federated optimization/aggregation (FedAvg, FedProx, SCAFFOLD) for non-IID data.
- Profile/optimize scalability (memory, latency, communication cost) and build simulation environments.
- Architect federated multi-task learning and handle task/feature heterogeneity (personalized models, meta-learning, gradient aggregation; prevent negative transfer).
- Create protocols for downstream adaptation/validation with per-task metrics and fairness assessment.
- Build multi-task small-molecule property prediction (ADMET, solubility, permeability, metabolic stability, off-target liabilities).
- Design/deploy generative chemistry models (VAEs, diffusion, flow matching, autoregressive) for de novo design, lead optimization, scaffold hopping.
- Implement integrated ADMET-driven, multi-objective design (Pareto-front exploration) and synthetic feasibility exploration.
- Apply interpretability/XAI and establish rigorous benchmarking and reproducible governance (public + proprietary data; publications/presentations).
Basic Qualifications
- PhD in CS, Computational Chemistry, Cheminformatics, ML, Computational Biology, or related field.
- 5+ years post-PhD ML experience in drug discovery (preference for 8+ years) or equivalent technical leadership/impact.
Additional Preferences
- Technical leadership and mentoring without formal people-management.
- Generative molecular design; multi-task/representation-learning track record; medicinal chemistry + ADMET optimization.
- Hands-on federated learning/distributed optimization/privacy-preserving ML; top-tier publications.
- Graph/geometric deep learning; organic chemistry + synthetic feasibility; fragment/structure-based design.
- PK/PD modeling and clinical translation; RDKit/DeepChem and PyTorch; active learning and D-M-T-A cycles.
- Uncertainty quantification and XAI in federated/multi-task settings; exceptional cross-disciplinary communication; independent, self-directed.
Other Information
- Location: Indianapolis, San Francisco, or Boston; up to 10% travel; attendance expected at key conferences.