Jobs at Eli Lilly and Company

Lead Methods4Insight to define, validate, and deploy analytical and computational methods for molecule discovery. Build/guide a multidisciplinary team, set evaluation frameworks, prioritize build-vs-buy decisions, fill strategic data gaps, and integrate agent-ready AI capabilities across Data Foundry.

Lead technical vision and execution for AI capabilities in drug discovery, build a multidisciplinary team, and drive organizational alignment with research priorities.

The Scientific Data Engineer will design data architecture, build ETL/ELT pipelines, implement data quality checks, and develop semantic layers for AI-consumable data products in healthcare research.

Embed with discovery teams to translate scientific workflows into production AI systems. Rapidly prototype and deploy LLM/RAG-based solutions for multi-omics problems, run evaluation loops, ensure data provenance and reliability, and distill learnings into reusable platform components to drive adoption across therapeutic areas.

The Engineer will operationalize scientific tools and methods within the Data Foundry, building deployment pipelines, model serving infrastructure, and ensuring APIs are robust and analytics-ready for both scientists and AI agents.

The Senior Director, Software Engineering will lead architecture and design, manage engineering teams, and drive adoption of platform standards while ensuring high-quality software delivery across Lilly's systems.

Lead development, deployment, and validation of advanced analytical methods (e.g., cheminformatics, computational biology, AI/ML) to inform discovery decisions, integrate tools with data infrastructure and autonomous agents, mentor junior scientists, publish research, and collaborate across Data Foundry pillars to accelerate molecule discovery.

Develop scientific software for AI-native drug discovery, building data pipelines, APIs, and lab automation integrations while collaborating with scientists.

The Scientific Lead will drive oncology target discovery using genetic medicine technologies, design experimental strategies, collaborate with teams, and contribute to innovative cancer therapies.

Own and build end-to-end product surfaces that wrap Lilly's AI models into reliable, scalable SaaS workflows for biotech customers. Ship frontend, backend, data, and integrations; drive features from fuzzy requirements to production; mentor peers; influence architecture and engineering standards; work with product, design, and data science; navigate enterprise security, compliance, and platform integrations; participate in code review, testing, release, and on-call.

Lead design and build of an end-to-end SaaS product that wraps Lilly's AI/ML and LLM models for biotech customers. Architect frontend, backend, data and integrations, implement federated learning, establish engineering practices (testing, releases, on-call), mentor hires, recruit, and coordinate with internal platform, security, and customer stakeholders.

Founding product designer for an AI/ML drug-discovery platform serving biotech partners. Own discovery, user research, workflows, interaction and visual design, and a pragmatic design system. Partner closely with product, engineering, and data science to validate ideas, prototype (including code), ship polished UX, mentor teammates, and represent design to external scientific partners and enterprise stakeholders.

Lead and grow a molecular pharmacology team to design robust screening cascades, deliver high-quality pharmacology and mechanistic data, drive program decisions, and partner cross-functionally. Provide scientific leadership, mentor scientists, implement AI/automation tools, ensure safety and compliance, and accelerate small-molecule drug discovery toward patient-ready candidates.

The Data Architect will design data infrastructure for AI-native drug discovery, focusing on data models, ontologies, and data platform architecture to support scientific workflows and analytics.

The Technical Lead will build software systems for AI-native drug discovery, design data pipelines, develop APIs, and collaborate with scientists to create efficient workflows and solutions.

Lead design and delivery of production LLM systems for drug discovery: build RAG pipelines, hybrid retrieval, text-to-SQL, agentic workflows, evaluation frameworks, and orchestration for scientific analyses.

The role requires engineering integrative solutions between AI and lab systems to enhance molecule discovery through automation and data connectivity. It involves prototyping, collaboration with scientists, deploying intelligent systems, and ensuring reliable lab operations.

The Senior Cloud Platform Engineer will operate and improve cloud platforms for bioinformatics workflows, debug complex systems, and collaborate with scientists.

The Data Architect will design and build data infrastructure to support AI-driven drug discovery, focusing on data models, architectures, and integration strategies.

Lead the development of novel AAV therapies, implement strategic vision, collaborate across departments, and manage a team for gene therapy advancements.