Sr. Principal or Engineering Advisor - Agentic Lab Automation Integration
Eli Lilly and Company
August 23, 2026
Remote friendly (San Diego, CA)
United States
$132,000 - $222,200 USD yearly
IT
Position Summary
- Engineer the connective tissue between agentic AI and physical lab systems, integrating with robotic platforms, analytical instruments, and data pipelines.
- Design agent workflows that reason over experimental data, trigger automated actions, and surface insights to scientists.
- Prototype rapidly, productionize effective solutions, and collaborate with chemists, biologists, and automation engineers to deploy intelligent systems that accelerate molecule discovery.
Research & Innovation
- Build multi-agent systems with orchestration, state management, error recovery, and tool integration.
- Prototype and iterate on agent planning, memory systems, and human-in-the-loop patterns.
- Interface agent architectures with lab automation platforms (Hamilton, Tecan, Opentrons) for closed-loop execution.
Solution Deployment
- Transition prototypes into reliable lab operations with automation engineers and scientists.
- Deploy and maintain containerized services using Docker and Kubernetes with GitOps and CI/CD.
- Integrate cloud orchestration (e.g., Argo on Kubernetes) with laboratory control systems.
External Engagement
- Publish, give talks, review papers, and scout emerging trends in AI@Lilly and external communities.
- Evaluate vendors, open-source projects, and academic collaborations.
Basic Qualifications
- PhD (or MS + 2 yrs / BS + 5 yrs) in Chemical/Mechanical Engineering, Robotics, Computer Engineering, or related field; demonstrated wet-lab automation experience.
- Experience with laboratory automation systems and LIMS engineering.
- Experience integrating software control and/or AI systems with lab automation (liquid handlers, analytical instruments, robotic workflows).
- Strong containerization (Docker) and Kubernetes orchestration experience in production.
- Scalable production Python applications (e.g., Redis, FastAPI, Flask/Streamlit, pytest).
Preferred
- LLM post-training (fine-tuning, RLHF).
- Integrate AI/decision systems with lab automation.
- Build/maintain translation layer between planning logic and instrument control.
- Research contributions/publications (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR).
- Engineer the connective tissue between agentic AI and physical lab systems, integrating with robotic platforms, analytical instruments, and data pipelines.
- Design agent workflows that reason over experimental data, trigger automated actions, and surface insights to scientists.
- Prototype rapidly, productionize effective solutions, and collaborate with chemists, biologists, and automation engineers to deploy intelligent systems that accelerate molecule discovery.
Research & Innovation
- Build multi-agent systems with orchestration, state management, error recovery, and tool integration.
- Prototype and iterate on agent planning, memory systems, and human-in-the-loop patterns.
- Interface agent architectures with lab automation platforms (Hamilton, Tecan, Opentrons) for closed-loop execution.
Solution Deployment
- Transition prototypes into reliable lab operations with automation engineers and scientists.
- Deploy and maintain containerized services using Docker and Kubernetes with GitOps and CI/CD.
- Integrate cloud orchestration (e.g., Argo on Kubernetes) with laboratory control systems.
External Engagement
- Publish, give talks, review papers, and scout emerging trends in AI@Lilly and external communities.
- Evaluate vendors, open-source projects, and academic collaborations.
Basic Qualifications
- PhD (or MS + 2 yrs / BS + 5 yrs) in Chemical/Mechanical Engineering, Robotics, Computer Engineering, or related field; demonstrated wet-lab automation experience.
- Experience with laboratory automation systems and LIMS engineering.
- Experience integrating software control and/or AI systems with lab automation (liquid handlers, analytical instruments, robotic workflows).
- Strong containerization (Docker) and Kubernetes orchestration experience in production.
- Scalable production Python applications (e.g., Redis, FastAPI, Flask/Streamlit, pytest).
Preferred
- LLM post-training (fine-tuning, RLHF).
- Integrate AI/decision systems with lab automation.
- Build/maintain translation layer between planning logic and instrument control.
- Research contributions/publications (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR).