Advisor - Scientific Machine Learning & Agentic Workflows Engineer

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us. Organization Overview: At Eli Lilly and Company, we unite caring with discovery to make life better for people around the world. Lilly is a leader in global healthcare and has been discovering and developing medicines that help improve lives for more than 140 years. Our 50,000 employees around the world work as a team to bring breakthrough medicines to patients who need them. We are looking for motivated, highly skilled scientists and engineers to help us continue to bring breakthrough medicines to patients. Delivery, Devices, and Connected Solutions (DDCS) sits within 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. Computational Modeling & Simulation (M&S) is a disciplinary horizontal within DDCS that builds and applies predictive models spanning molecular to system scales. M&S outputs inform device architecture, formulation and delivery-system design, and development strategy — reducing empirical iteration, quantifying risk, and building mechanistic understanding of how our delivery systems perform in real use. The scope of the capability spans continuum-scale multiphysics (CFD and FEA), molecular and mesoscale simulation, and scientific machine learning, working closely with Formulation Sciences, Device Engineering, Analytical Sciences, Manufacturing, Quality, and Regulatory. Position Overview: This role builds hybrid physics-and-data models — and the agentic software layer that puts them in the hands of working scientists. It has two connected halves. The first is scientific machine learning: physics-informed networks, operator learning, multi-fidelity surrogates, Bayesian calibration, and gray-box system identification that accelerate or extend physics-based simulation. The second is AI engineering: agentic workflows that plan, set up, execute, and post-process modeling and simulation tasks by calling real solvers and real data, so that a scientist can move from question to credible answer without hand-assembling every step. The role sits within the Computational Modeling & Simulation team in DDCS and works across the programs that the team supports. This is not a standalone research role: the models and tools are built with and for the drug product, device, and process development functions across Product Research & Development that use them. You will independently design, implement, validate, and support the workflows you build, working closely with the DDCS AI Application Development and Data Sciences functions on architecture, platform choices, and compliance. Key Responsibilities: Scientific Machine Learning Development Independently design, train, and validate SciML models — physics-informed neural networks, operator-learning architectures (e.g., DeepONet, Fourier neural operators), and Gaussian-process or multifidelity surrogates — against high-fidelity simulation and experimental data. Apply Bayesian calibration and uncertainty quantification to deliver predictions with a defensible confidence statement rather than a point estimate. Apply gray-box identification and symbolic-regression methods to infer unknown parameters or missing mechanisms from sparse experimental data. Agentic Modeling Workflow Engineering Design and build LLM-based agentic workflows that plan, set up, execute, monitor, and post-process modeling tasks by calling real tools — solvers (e.g., Abaqus, COMSOL, Ansys, OpenFOAM, LAMMPS, GROMACS), meshing and geometry utilities, HPC schedulers, and internal data services. Implement the tool interfaces, APIs, and retrieval layers that connect agents to DDCS model libraries, simulation archives, and structured data sources. Define and enforce human-in-the-loop checkpoints at the points where a modeling decision requires expert judgment rather than automation. Package workflows so that a scientist who is not a software developer can use them reliably and unaided. Credibility, Traceability, and Guardrails Ensure every agent-executed run emits a complete provenance record: inputs, geometry and discretization, solver and library versions, convergence evidence, random seeds, and the human approvals applied. Design for deterministic replay — any result that informs a decision must be reproducible from its recorded provenance, within a documented tolerance. Build evaluation harnesses — benchmark problems with known solutions, regression tests, and reliability metrics — that quantify how often a workflow produces the right answer. Define credibility practice: defining defensible practice and credibility frameworks for hybrid physics-ML models in the spirit of ASME V&V 40 and the FDA's 2023 guidance on assessing computational model credibility. Software Engineering, Deployment, and Cross-Functional Delivery Write maintainable, tested code; treat version control, code review, CI/CD, and containerization as defaults. Deploy and operate workflows on HPCs and approved cloud environments, instrumented for observability, latency, and cost. Deploy in partnership with the functions that will use the output — device engineering, drug product and process development, analytical sciences, manufacturing, quality, and regulatory — so that tools fit existing development workflows, data sources, and decision timelines rather than requiring users to change how they work. Apply business judgment when setting priorities: understand the portfolio, program milestones, and decision gates the modeling supports, and direct effort toward the questions where a faster or better answer changes a development decision. Make and defend practical trade-offs on build versus buy, model fidelity versus cost and turnaround time, and automation versus expert review, keeping total cost of ownership and the needs of downstream users in view. Apply responsible AI, security, and data-handling controls across the workflow lifecycle, in partnership with IT, Quality, and Information Security. Partnership, Enablement, and Communication Work as an embedded member of cro