Director, Molecular AI & Federated Learning

Job Summary

• Director, Molecular AI & Federated Learning (Tune Lab)
• 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).

• 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 (Fed Avg, Fed Prox, 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, Pub Chem, proprietary data), publish/present, and uphold reproducible code/version control.

• 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.

• 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/Deep Chem and PyTorch.
• Active learning and design–make–test–analyze; uncertainty quantification and XAI.
• Strong communication, learning agility, independent drive.

• Indianapolis, San Francisco, or Boston; up to 10% travel.

• Company bonus (company/individual performance dependent).
• 401(k); pension; vacation; medical/dental/vision/prescription; flexible benefits; life insurance; time off/leave; well‑being benefits.

• Anticipated wage: $177,000–$281,600.