Advisor - Scientific Machine Learning & Agentic Workflows Engineer
Eli Lilly and Company1 day ago
Indianapolis, IN, USASenior
Base Salary
$131k - $211k/yr
Responsibilities
- Design, train, validate, and apply scientific machine learning models against high-fidelity simulation and experimental data.
- Build LLM-based agentic workflows that plan, execute, monitor, and post-process modeling tasks through solvers, utilities, HPC schedulers, and internal data services.
- Implement tool interfaces, APIs, retrieval layers, human-in-the-loop checkpoints, and usable workflow packages for scientists.
- Create complete provenance records, deterministic replay capabilities, evaluation harnesses, regression tests, and reliability metrics.
- Deploy and operate workflows on HPC clusters and approved cloud environments with observability, latency, and cost monitoring.
- Partner with drug product, device, process development, analytical sciences, manufacturing, quality, regulatory, data science, and digital transformation teams.
- Support credibility frameworks, responsible AI, security, data-handling controls, GxP, and 21 CFR Part 11 considerations.
- Train and support users and measure workflow adoption.
Requirements
- PhD in applied mathematics, computer science, machine learning, computational mechanics or physics, chemical, mechanical, biomedical engineering, or a related field.
- Doctoral research centered on scientific machine learning, such as physics-informed learning, operator learning, surrogate modeling, or hybrid mechanistic-ML methods.
- Demonstrated experience developing and validating SciML models against physics-based simulation or experimental data.
- Strong Python software engineering experience, including PyTorch or JAX and Git-based collaborative development.
- Hands-on experience building LLM-enabled workflows with multi-step agent orchestration and tool/function calling or retrieval-augmented generation.
- Experience running computational work on HPC clusters or cloud infrastructure.
- Peer-reviewed publications or open-source contributions in scientific machine learning.
- Preferred experience with neural operators, multifidelity modeling, Bayesian uncertainty quantification and calibration, active learning, Bayesian optimization, gray-box identification, symbolic regression, or biomedical and engineering applications.
- Preferred experience with agent frameworks, tool-interoperability standards, production deployment, containers, CI/CD, observability, simulation solvers, and their scripting interfaces.
- Familiarity with model credibility practices, ASME V&V 40, FDA computational model credibility guidance, GxP, and 21 CFR Part 11.
- Front-end or full-stack experience sufficient to build usable scientific-tool interfaces is preferred.
- Domain exposure to drug delivery, medical devices, combination products, or biomedical transport problems is preferred.
Benefits
- Full-time employees are eligible for a company bonus depending partly on company and individual performance.
- Benefits include 401(k), pension, vacation, medical, dental, vision, prescription drug, flexible spending, life insurance, leave, well-being, fitness, employee assistance, and employee club benefits.
- The role is located in Indianapolis, Indiana, at Lilly Technology Center – North, with 0–10% travel.
Categories
About Eli Lilly and Company
Eli Lilly and Company researches, develops, manufactures, and markets prescription medicines and biologics for patients, sold through healthcare providers, pharmacies, and payers. Core therapeutic areas include diabetes, oncology, immunology, neuroscience, and obesity. Founded in 1876 and headquartered in Indianapolis, it is a public company (NYSE: LLY) with global operations and is building a new advanced manufacturing site for gene therapy and other modalities in Lebanon, Indiana.