Principal Clinical Development AI Engineer

Eli Lilly Eli Lilly · Pharma · Indianapolis, IN

Principal Clinical Development AI Engineer at Eli Lilly to design, build, and deploy AI-powered solutions for clinical development workflows, partnering with statisticians and data engineers. The role involves identifying opportunities, building AI tools (including generative and multi-agent systems), establishing trust standards, and communicating results. Requires experience in AI/ML development and building adopted AI tools, with a focus on regulatory compliance and therapeutic area understanding.

What you'd actually do

  1. Partner with statisticians, statistical programmers, and data engineers to identify high-value opportunities for AI-driven automation across clinical development workflows.
  2. Decompose complex analytical and operational processes into components suitable for AI assistance, ranging from straightforward productivity tools to sophisticated multi-agent systems.
  3. Build, test, and iterate on AI solutions — including generative tools, review and quality-checking tools, and integrated workflows that combine both.
  4. Establish appropriate trust and validation standards for AI outputs, calibrated to the risk profile of each use case.
  5. Stay current with the rapidly evolving AI landscape and translate emerging capabilities into practical applications for clinical development.

Skills

Required

  • MS in statistics, biostatistics, computer science, data science, or a related quantitative STEM field
  • Three or more years of experience in AI/ML development, or biostatistics
  • Demonstrated ability to build AI-powered tools or workflows that solve real problems for end users
  • Strong software development skills with experience in at least one of: R, Python, or modern AI/ML frameworks

Nice to have

  • Experience in pharmaceutical or clinical development settings
  • Direct experience as a biostatistician or statistical programmer in clinical development
  • Hands-on experience with large language models, agentic AI systems, or multi-agent architectures
  • Track record of building tools that were adopted by their intended users
  • Familiarity with regulatory expectations around statistical deliverables in drug development
  • Experience designing review or quality-control workflows where AI output reliability is critical
  • Interpersonal communication skills

What the JD emphasized

  • AI-powered solutions
  • AI-driven automation
  • multi-agent systems
  • AI solutions
  • AI outputs
  • AI landscape
  • AI-powered workflows
  • AI tool outputs
  • AI solution demonstrations
  • AI-assisted analytical workflows
  • AI solutions
  • AI tools
  • AI solutions
  • AI Leadership
  • AI projects
  • AI
  • AI/ML development
  • AI-powered tools
  • AI systems
  • AI output reliability

Other signals

  • AI-powered solutions
  • accelerate the work of statisticians
  • AI-powered workflows
  • generative tools
  • multi-agent systems