Scientific Lead - Forward Deployed AI Engineer, Applied Intelligence Discovery

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.

We are building something unprecedented — an AI foundation that will push the frontier on what is possible today across drug discovery research, from target identification and disease biology through translational science.

The Applied Intelligence for Discovery (AI4D) team is a newly formed group within Lilly Research Laboratories that operates at the intersection of scientific delivery and core platform development. AI4D’s mission is to connect scientists to petabyte-scale data through natural language interfaces, automated analysis workflows, and intelligent search — and to convert early deployments into repeatable system standards and evaluation practices that scale across therapeutic areas.

The Forward Deployed AI Engineer is the connective tissue between what AI can do and what discovery scientists need it to do. You will embed directly with research teams across therapeutic areas translating real-world data, infrastructure, and scientific constraints into production systems that collapse the time from question to answer from days or weeks to minutes. You will measure success through production adoption, measurable workflow impact, and eval-driven feedback loops that inform platform and model roadmaps.

This is not a traditional software engineering or data science role. You will sit with biologists, geneticists, and computational scientists working with petabytes of multi‑omics data, leading end‑to‑end deployments from scoping through sustained production use.

• Embed with computational biology and disease biology teams in your assigned therapeutic area to develop deep understanding of their workflows, data, tools, and bottlenecks
• Translate use‑cases into concrete, testable prototypes with clear success criteria. Rapidly turn ideas into a working demo, complete with evaluation benchmarks that tighten acceptance criteria over time
• Design and ship production systems quickly that solve specific scientific problems; owning integrations, data provenance, reliability, and on‑call readiness
• Apply LLM, retrieval‑augmented generation (RAG), text‑to‑SQL, agentic AI frameworks, and other emerging approaches to drug discovery challenges including target identification, biomarker prioritization, mechanism of action studies, and extraction of insight from large‑scale multi‑omics datasets
• Run evaluation loops that measure model and system quality against workflow‑specific scientific benchmarks; use results to drive model selection, product changes, and iterative evidence generation that tightens acceptance criteria over time
• Distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across therapeutic areas and accelerate future development
• Partner closely with AI/LLMOps engineers on the AI4D team to ensure your field‑tested solutions feed back into the platform as reusable components, not one‑off builds
• Contribute to a culture of experimentation, speed, and evidence‑based impact measurement within the AI4D group and the broader LRL research community

• PhD in computational biology, bioinformatics, data science, computer science, or a related field, with 3+ years of software/ML engineering or technical deployment experience; or equivalent demonstrated experience building and deploying AI/ML tools for scientific applications in biotech, pharma, or scientific software; MSin computational biology, bioinformatics, data science, computer science, or a related field, with 5+ years of software/ML engineering or technical deployment experience;
  
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