Technical Lead

Scientific Software DeveloperAt 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.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.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 View email address on click.appcast.io for enterprise scaling and maintenance—keeping you focused on innovation and the next high-impact problem.ResponsibilitiesDesign, 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 OperationalizationBuild 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 InfrastructureDevelop 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 IntegrationBuild 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 & View email address on click.appcast.io HandoffWork directly with bench scientists to rapidly prototype custom applications, dashboards, and workflow tools to improve scientist's experience and efficiencyValidate prototypes through iterative scientist feedback, then partner with View email address on click.appcast.io to hand off for enterprise scaling with defined transition criteria and documentation.Cloud Infrastructure & DevSecOpsBuild and operate cloud-native components (AWS, Azure, or GCP) supporting containerized workflows (Kubernetes/Docker), infrastructure-as-code, CI/CD, and workflow orchestration (Prefect, Airflow, Nextflow).Apply DevSecOps standards including security scanning, code review, and automated testing.Basic RequirementsB.S./M.S/Phd. in Computer Science, Bioinformatics, Computational Biology, Cheminformatics, Chemistry, Biology, Biomedical Engineering, or related STEM field.BS (with 10+years), MS (with 5+ years) or Phd (1+ year) of scientific software development experience, with understanding of experimental data types and scientific workflows.Proficiency in Python and at least one additional language (Java, C#, Go, TypeScript, or Rust); strong SQL skills.Experience building RESTful APIs, data pipelines, and/or microservices for scientific or technical applications.Preferred QualificationsPharmaceutical or biotech research industry experience, particularly in discovery workflows for biology, chemistry, biochemistry or automation.MLOps tooling: experiment tracking (MLflow, W&B), model registries, model serving, monitoring/drift detection.Familiarity with cloud platforms (AWS, Azure, or GCP), containerization (Docker/Kubernetes), and Git.Strong communication skills with a track record of productive scientist collaboration.Exposure to AI agent infrastructure, MCP frameworks, or building APIs that AI/ML systems invoke programmatically.Experience integrating lab automation systems with digital platforms or AI-driven workflows.Hands-on experience with cheminformatics tools (RDKit, Schrödinger, MOE) or bioinformatics platforms.Data warehousing experience (Postgres, Redshift, BigQuery, Snowflake) and scientific data standards/ontologies.LIMS/ELN experience (e.g., Benchling) and laboratory instrument integration.Workflow orchestration (Prefect, Airflow, Nextflow, WDL), CI/CD, and Linux/bash scripting.Strong learning agility—willingness to step outside comfort zone and adopt new technologies to get the job done.