Advisor - Scientific Machine Learning Scientist - Drug Delivery Device Development

At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We’re looking for people who are determined to make life better for people around the world.

Organization Overview:

At Lilly, we serve an extraordinary purpose. We make a difference for people around the globe by discovering, developing and delivering medicines that help them live longer, healthier, more active lives. Not only do we deliver breakthrough medications, but you also can count on us to develop creative solutions to support communities through philanthropy and volunteerism.

Position Overview:

Delivery, Devices, and Connected Solutions (DDCS) sits within Eli Lilly’s Product Research & Development organization. We are a diverse team of scientists and engineers responsible for discovering, designing, and developing patient-centric drug delivery solutions across a broad range of modalities — from injection devices to novel routes of administration and nanomedicines. DDCS drives the drug delivery innovation agenda across early and late development to meet the needs of an expanding portfolio that spans small molecules, biologics, and nucleic acid therapeutics.

DDCS is organized around a matrix model with strong disciplinary and functional horizontals supporting innovation and commercialization verticals. Our vision is to get our medicines to more patients faster by accelerating reach and scale, guided by three strategic pillars: Delivery Systems, Robust & Sustainable, and Patient Experience + Outcomes.

The Modeling & Simulation team within DDCS advances predictive modeling capabilities across molecular-to-system scales and single-to-multi-physics domains, integrating scientific machine learning (SciML) and AI to accelerate design, de-risk development, and deepen mechanistic understanding for drug delivery systems.

We are seeking an innovative Advisor - Scientific Machine Learning Scientist to join the Modeling & Simulation team. This role uniquely combines deep domain expertise in physics and engineering with cutting-edge machine learning techniques to solve complex scientific problems that traditional approaches cannot address. You will develop physics-informed neural networks, hybrid models, and multi-scale modeling solutions that accelerate device innovation, optimize formulations, and enhance patient outcomes — while communicating insights to senior leadership to drive strategic decisions.

This is a hands-on technical role that combines model development, capability building, and cross-functional collaboration to inform decisions from molecular interactions and material behavior to fluid/solid mechanics, device performance, and patient-use conditions.

Responsibilities:

Scientific Model Development & Deployment

• Design & Build Physics-Informed Models: Develop physics-informed neural networks (PINNs), operator learning architectures (DeepONets, FNOs, GNOs), and hybrid modeling approaches that combine mechanistic/first-principles models with data-driven ML components to capture complex phenomena in device performance, drug release kinetics, and patient interactions.

• Multi-Scale & Multi-Physics Integration: Build models that integrate information from molecular to device to patient levels, incorporating temporal dynamics and heterogeneous data sources; create surrogate models that efficiently approximate expensive computational simulations (FEM, CFD) to enable rapid design space exploration.

• Uncertainty Quantification & Decision Support: Implement Bayesian approaches, ensemble techniques, Gaussian processes, and active learning to provide confidence bounds critical for medical device safety decisions and regulatory submissions.

• Deploy & Scale on Modern Compute: Leverage HPC/GPU clusters and cloud infrastructure to develop, test, and deploy models; champion software engineering best practices (version control, CI/CD, testing, reproducibility, MLOps).

Use Case Identification & Solution Architecture

• Partner Across Disciplines: Collaborate with drug delivery scientists, device engineers, fo