Director, Molecular AI & Federated Learning
Eli Lilly and Company
September 03, 2026
On-site
San Francisco, CA
IT
Job Summary
- Director, Molecular AI & Federated Learning (TuneLab)
- Set the technical vision uniting privacy-preserving federated learning with generative small-molecule design; lead predictive and generative models that accelerate small-molecule lead optimization and candidate selection.
- Lead through vision, methodological rigor, and mentorship (no formal people management).
Key Responsibilities
- Define technical direction and research agenda for federated learning and molecular AI aligned to platform/portfolio priorities.
- Provide principal technical leadership and mentor data scientists and engineers; guide experimental design and review methods/code.
- Architect federated foundation models (e.g., Transformer and graph neural network–based) for large-scale federated pre-training.
- Advance semi-supervised/self-supervised learning for federated constraints (communication bottlenecks, data heterogeneity).
- Develop robust federated optimization/aggregation strategies (FedAvg, FedProx, SCAFFOLD) for non-IID data.
- Optimize scalability (memory, latency, communication cost) and build simulation environments to benchmark federated strategies.
- Architect federated multi-task learning models for shared representations across endpoints.
- Design algorithms for task/feature heterogeneity (personalization, meta-learning, gradient aggregation, regularization to 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, stability, off-target liabilities).
- Design/deploy generative chemistry models (VAEs, diffusion, flow matching, autoregressive) for de novo design/optimization/scaffold hopping.
- Develop ADMET-driven multi-objective prediction–generation pipelines (Pareto-front exploration).
- Ensure synthetic feasibility via reaction-aware generation, retrosynthetic planning integration, and collaboration with synthetic chemists.
- Learn structure–activity and representations from sparse/noisy data; apply XAI for scientific insight.
- Establish benchmarks (ChEMBL, ZINC, PubChem, proprietary data), publish/present, and uphold reproducible code/version control.
Basic Qualifications
- PhD in Computer Science, Computational Chemistry, Cheminformatics, Machine Learning, Computational Biology, or related field.
- 5+ years post-PhD ML drug-discovery experience (preference for 8+); or equivalent technical leadership/impact.
Additional Preferences
- Technical leadership without formal people-management requirement.
- Track record in generative molecular design; multi-task/representation learning.
- Deep medicinal chemistry and ADMET optimization knowledge.
- Hands-on federated learning, distributed optimization, privacy-preserving ML.
- Publications in top venues; expertise in GNNs/geometric deep learning.
- Organic chemistry and synthetic feasibility; fragment-/structure-based drug design.
- PK/PD knowledge; RDKit/DeepChem and PyTorch.
- Active learning and design–make–test–analyze; uncertainty quantification and XAI.
- Strong communication, learning agility, independent drive.
Other Information / Location
- Indianapolis, San Francisco, or Boston; up to 10% travel.
Benefits (explicitly stated)
- Company bonus (company/individual performance dependent).
- 401(k); pension; vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits.
Pay Transparency (explicitly stated)
- Anticipated wage: $177,000–$281,600.
- Director, Molecular AI & Federated Learning (TuneLab)
- Set the technical vision uniting privacy-preserving federated learning with generative small-molecule design; lead predictive and generative models that accelerate small-molecule lead optimization and candidate selection.
- Lead through vision, methodological rigor, and mentorship (no formal people management).
Key Responsibilities
- Define technical direction and research agenda for federated learning and molecular AI aligned to platform/portfolio priorities.
- Provide principal technical leadership and mentor data scientists and engineers; guide experimental design and review methods/code.
- Architect federated foundation models (e.g., Transformer and graph neural network–based) for large-scale federated pre-training.
- Advance semi-supervised/self-supervised learning for federated constraints (communication bottlenecks, data heterogeneity).
- Develop robust federated optimization/aggregation strategies (FedAvg, FedProx, SCAFFOLD) for non-IID data.
- Optimize scalability (memory, latency, communication cost) and build simulation environments to benchmark federated strategies.
- Architect federated multi-task learning models for shared representations across endpoints.
- Design algorithms for task/feature heterogeneity (personalization, meta-learning, gradient aggregation, regularization to 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, stability, off-target liabilities).
- Design/deploy generative chemistry models (VAEs, diffusion, flow matching, autoregressive) for de novo design/optimization/scaffold hopping.
- Develop ADMET-driven multi-objective prediction–generation pipelines (Pareto-front exploration).
- Ensure synthetic feasibility via reaction-aware generation, retrosynthetic planning integration, and collaboration with synthetic chemists.
- Learn structure–activity and representations from sparse/noisy data; apply XAI for scientific insight.
- Establish benchmarks (ChEMBL, ZINC, PubChem, proprietary data), publish/present, and uphold reproducible code/version control.
Basic Qualifications
- PhD in Computer Science, Computational Chemistry, Cheminformatics, Machine Learning, Computational Biology, or related field.
- 5+ years post-PhD ML drug-discovery experience (preference for 8+); or equivalent technical leadership/impact.
Additional Preferences
- Technical leadership without formal people-management requirement.
- Track record in generative molecular design; multi-task/representation learning.
- Deep medicinal chemistry and ADMET optimization knowledge.
- Hands-on federated learning, distributed optimization, privacy-preserving ML.
- Publications in top venues; expertise in GNNs/geometric deep learning.
- Organic chemistry and synthetic feasibility; fragment-/structure-based drug design.
- PK/PD knowledge; RDKit/DeepChem and PyTorch.
- Active learning and design–make–test–analyze; uncertainty quantification and XAI.
- Strong communication, learning agility, independent drive.
Other Information / Location
- Indianapolis, San Francisco, or Boston; up to 10% travel.
Benefits (explicitly stated)
- Company bonus (company/individual performance dependent).
- 401(k); pension; vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well-being benefits.
Pay Transparency (explicitly stated)
- Anticipated wage: $177,000–$281,600.