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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 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 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.

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