Engineering Advisor - Agentic Lab Automation Integration

Role Summary

We are rebuilding the Design-Make-Test-Analyze (DMTA) cycle by integrating agentic AI, cloud-based orchestration, and LIMS infrastructure to connect experimental readouts with tools that accelerate laboratory science. You will engineer the connective tissue between Agentic AI and physical lab systems building practical integrations with robotic platforms, analytical instruments, and data pipelines. You'll design agent workflows that reason over experimental data, trigger automated actions, and surface insights to scientists. This is a hands-on engineering role: you'll prototype rapidly, productionize what works, and collaborate with chemists, biologists, and automation engineers to deploy intelligent systems that accelerate molecule discovery tasks.

Responsibilities

• Build multi-agent systems with robust orchestration, state management, error recovery, and tool integration
• Prototype and iterate rapidly on agent planning strategies, memory systems, and human-in-the-loop patterns
• Design agent architectures that interface with lab automation platforms (Hamilton, Tecan, Opentrons) for closed-loop experimental execution
• Partner with automation engineers and scientists to transition prototypes into reliable lab operations
• Deploy and maintain containerized services using Docker and Kubernetes with GitOps and CI/CD practices
• Integrate cloud-based orchestration frameworks such as Argo on Kubernetes with laboratory control systems
• Represent Frontier AI in the broader AI@Lilly and external AI research community: publish, give talks, review papers, and scout emerging trends
• Evaluate external vendors, open-source projects, and academic collaborations for strategic fit
• Autonomous agents reliably execute experiments on physical laboratory instruments
• Measurable reduction in DMTA turnaround through autonomous planning and execution
• Agents you build become indispensable to the automation engineering community
• Frontier AI platforms scale from pilot deployments to production lab environments

Qualifications

• Required: PhD (or MS + 2 yrs / BS + 5 yrs equivalent experience) in Chemical / Mechanical Engineering, Robotics, Computer Engineering, or related discipline with demonstrated wet-lab automation experience
• Required: Demonstrated experience with laboratory automation systems and LIMS engineering
• Required: Direct experience integrating software control and/or AI systems with lab automation platforms (liquid handlers, analytical instruments, robotic workflows)
• Required: Strong experience with containerization (Docker) and Kubernetes-based orchestration in production environments
• Required: Experience building scalable and production-level Python applications using tools like Redis, FastAPI, Flask/Streamlit, pytest

• Preferred: Experience with LLM post-training, fine-tuning, or RLHF
• Preferred: Direct experience integrating AI or algorithmic decision systems with laboratory automation
• Preferred: Proven ability to build and maintain the translation layer between high-level planning logic and low-level instrument control
• Preferred: Demonstrable research experience, evidenced by contributions to projects, and ideally through publications in relevant ML/NLP venues

Education

• PhD (or MS + 2 yrs / BS + 5 yrs equivalent experience) in Chemical / Mechanical Engineering, Robotics, Computer Engineering, or related discipline