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# Autonomous Infrastructure and Robotic Science Lead

- **Company:** Argonne National Laboratory
- **Location:** Lemont, IL, United States
- **Experience:** Expert
- **Salary:** $116,250.0 - $181,350.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Infrastructure, Open Source Technology, Rapid Prototyping Process, Software Systems, RPL (Programming Language), Reinforcement Learning, Digital Twin, High Performance Computing, Generative AI, Information Technology, Data Analytics, Machine Learning Operations
- **Published:** June 18, 2026
- **Apply:** https://argonne.wd1.myworkdayjobs.com/Argonne_Careers/job/Lemont-IL-USA/Autonomous-Infrastructure-and-Robotic-Science-Lead_422956

## About the Role

* Completed Ph.D. in Computer Science, Materials Science, Physics, Chemistry, or a related field, and a minimum of 4+ years of related experience
 * Proven research track record in deploying automated and autonomous platforms and AI/ML towards accelerating science
 * Demonstrated ability to formulate scientific problems relevant to the DOE portfolio
 * Strong oral and written communication skills, with the ability to work effectively with internal and external collaborators to achieve established goals
 * Demonstrated ability to collaborate in a multidisciplinary environment and provide scientific guidance to a diverse research community
 * Ability to model Argonne's core values of impact, safety, respect, integrity, and teamwork

## Description

The Computing, Environment, and Life Sciences (CELS) Directorate seeks an outstanding scientist to lead and support frontier research at the intersection of AI, autonomous platforms, data infrastructure, and domain science. The candidate will have established expertise across automated and autonomous experimental platforms and AI in addition to leadership of multi-disciplinary research programs and the development of novel research concepts.

The scientist will lead Argonne's Rapid Prototyping Laboratory (RPL), a team of computer scientists, roboticists, data scientists, and subject matter experts, who develop hardware and software infrastructure for laboratory autonomy, support autonomous laboratories in domains including chemistry, biology, and quantum science, work with domain scientists to execute autonomous experiments, and advance laboratory autonomy and robotics.

RPL develops the open-source Modular Autonomous Discovery for Science (MADSci) software framework for the orchestration of autonomous laboratories in addition to software infrastructure supporting the operation, training, and execution of robotic workflows. The scientist would be responsible for directing activities towards the advancement of these internal capabilities in addition to the support and development of collaborations across Argonne and beyond.

Focus Areas (expertise in one or more is highly desirable):

 * Autonomous laboratories for chemistry, materials, biology, etc.
 * AI/ML for predictive modeling and inverse design
 * Generative models, reinforcement learning, and agent-based approaches to streamline experimentation and accelerate discovery
 * Integration of HPC, data infrastructure, and ML pipelines for data-driven and autonomous research
 * Digital twins and simulation-augmented AI tools, * Guide the development of infrastructure for laboratory autonomy including physical autonomous laboratories, robotics laboratories, and software frameworks for autonomous science and robotics
 * Facilitate collaborations between the RPL and domain scientists across Argonne and partner institutions in the execution of successful autonomous science demonstrations
 * Facilitate collaborations between the RPL and teams at partner institutions developing autonomous science and robotics infrastructure
 * Guide the RPL team towards the advancement of laboratory autonomy and robotics
 * Publish in refereed journals and present at conferences, symposia, and seminars
 * Provide work direction and mentorship to postdoctoral appointees, research assistants, students, and technical staff
 * Execute all activities in compliance with Argonne's safety policies, Safeguards and Security policies, work rules, and safe practices


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