Eli Lilly currently has 36 active AI-related job listings. The majority of these roles, 56%, are focused on agents, with data-related positions making up another 28%. Engineering is the most frequent function for these hires. The company is primarily hiring in the United States and India. Frequent technology tags include agent orchestration, RAG, and model serving, indicating a focus on building and deploying AI systems.
Currently tracking 28 active AI roles, down 25% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $58k–$345k (avg $175k).
Eli Lilly currently has 36 active AI-related roles in our index. The most common open titles are: Director, Discovery Bioinformatics Oncology (2), Advisor - Agent Research, Advisor - Antibody Developability Validation & Benchmarking, Advisor - Data Architect, Data Foundry, Advisor - Lab Automation Software Engineer. Most positions are in Engineering and Research.
Eli Lilly's active AI hiring is concentrated in: agents (53%), data (25%), application (8%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Eli Lilly is hiring AI talent in: United States (29 roles), India (7 roles).
Job postings at Eli Lilly most frequently reference: agent orchestration, rag, model serving, llm observability, vector db.
In the past 30 days, Eli Lilly has posted 12 new AI-related roles. That is a -48% change versus the prior 30 days (23 → 12).
| Title | Stage | AI score |
|---|---|---|
| Advisor - RWE (m/f/d) This role focuses on developing and implementing Real-World Evidence (RWE) strategies within the healthcare domain, specifically at Eli Lilly. The Advisor will plan, conduct, and communicate results from Real-World and Health Outcomes studies, such as database studies and chart reviews. Responsibilities include managing RWE projects, collaborating with internal teams and external partners, and ensuring adherence to research principles and regulatory guidelines. The ideal candidate has a strong academic background (PhD or Master's) in a relevant field and prior experience in Real-World Research. | — | 0 |