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.

Location: San Diego, CA; San Francisco, CA; Boston, MA; Louisville, CO; Indianapolis, IN

Overview

Lilly Small Molecule Discovery is purpose-built to create molecules that make life better for people. Discovery Technology and Platforms (DTP) accelerates molecule discovery by building optimized foundational platforms, streamlining lab operations through advanced technologies and data connectivity, and investing in novel capabilities.

Data Foundry is a multidisciplinary team within DTP that enables AI-native drug discovery through four integrated pillars: Architecture4Insight (data infrastructure and scientific software), Methods4Insight (analytical and computational methods), Automation & Scale4Insight (lab automation and agentic workflows), and Preparedness4Insight (data governance and readiness). These pillars empower every Lilly scientist to make optimal decisions by providing seamless access to data, insights, and AI-driven capabilities—serving both human scientists and autonomous AI agents.

Position Summary

We are seeking a Scientific Software Developer to build the software systems that power AI-native drug discovery. You will work directly with front-line discovery scientists to translate their needs into fit-for-purpose prototypes, data pipelines, APIs, MLOps infrastructure, agentic platform components, and lab automation integrations.

This role works across Architecture4Insight, Methods4Insight, and Automation & Scale4Insight. A defining aspect is Data Foundry’s prototype-to-production operating model: you will rapidly build and validate solutions with scientists, then hand off mature prototypes to Tech@Lilly for enterprise scaling and maintenance—keeping you focused on innovation and the next high-impact problem.

Responsibilities

Scientific Data Pipelines, APIs & LIMS

• Design, build, and maintain data processing pipelines for complex scientific datasets (chemical, biological, HTE, and automation-generated data), ensuring FAIR compliance and machine-actionability.
• Develop RESTful APIs and microservices providing unified programmatic access to LIMS, ELNs, instruments, data warehouses (Postgres, Redshift, Snowflake), and analytical databases.
• Support continuous improvement of LIMS and adjacent systems to meet evolving scientific workflows, security, and scalability standards.

MLOps & Model Operationalization

• Build ML deployment pipelines—experiment tracking, model versioning (MLflow, W&B), containerized serving, monitoring, and automated retraining.
• Implement model observability: drift detection, performance alerting, and lifecycle management.
• Collaborate with Methods4Insight to operationalize cheminformatics, statistical, and AI/ML models as production APIs.

Agentic Platforms & AI Agent Infrastructure

• Develop agent-ready APIs with structured error handling, audit trails, and monitoring supporting agent autonomy and human oversight.
• Contribute to MCP servers or similar frameworks exposing Data Foundry capabilities to AI agents.
• Build software enabling closed-loop experimentation: agents design, automation executes, data flows back, models update.

Automation Software & Lab Integration

• Build integrations connecting lab automation equipment, scheduling systems, and instrument data streams to Data Foundry’s infrastructure with proper metadata and traceability.
• Create modular, reusable automation workflow components scientists can configure without writing code.

Scientific Prototyping & Tech@Lilly Handoff

• Work directly with bench scientists to rapidly prototype custom applications, dashboards, and workflow tools to improve scientist’s experience and efficiency
• Validate prototypes through