Sr. Principal or Engineering Advisor - Agentic Lab Automation Integration

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 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).