Postbaccalaureate Appointee - Robotics Software Developer, Rapid Prototyping Laboratory

Argonne National Laboratory • Lemont, IL

9d

About The Position

The Rapid Prototyping Laboratory (RPL) in the Data Science and Learning division builds self-driving laboratories in which robots, scientific instruments, and AI work together to conduct experiments with minimal human intervention. The Postbaccalaureate Appointee will develop robotics software for these laboratories and bring new robotic platforms into the laboratory environment.

The role involves integrating robotic hardware—including robotic arms, mobile manipulators, and humanoid platforms such as Dexmate—through the full process from initial setup to running experiments under laboratory orchestration software. The appointee will write and extend device modules and REST APIs in MADSci, Argonne’s open-source laboratory orchestration framework, so new platforms can be controlled through a common laboratory interface.

The position also includes sensor integration and data collection for robot learning. Work includes instrumenting robots with cameras, tactile sensors, and force sensors; collecting demonstration data using teleoperation devices; and supporting the training and evaluation of policies built on large robotics foundation models. The appointee will help assess which tasks are suitable for learned policies and which are better addressed with conventional control. The work often involves unfamiliar hardware and software, incomplete vendor documentation, and no established integration path. The appointee will collaborate with staff in robotics, software, and scientific domains, and contributions to MADSci will be released publicly.

Requirements

• Recently completed a bachelor’s degree in computer science, robotics, electrical engineering, mechanical engineering, or a related field.
• Proficiency in Python, including experience writing code that others can read and maintain.
• Hands-on experience with robotic hardware, such as robotic arms, mobile robots, or humanoid platforms, including setup, calibration, and troubleshooting.
• Experience with a robot middleware or control framework such as ROS or ROS 2, or with a vendor SDK.
• Experience developing or consuming REST APIs.
• Familiarity with version control and collaborative software development using Git.
• Ability to bring up unfamiliar hardware using vendor documentation and make progress when documentation is incomplete.
• Ability to communicate clearly with colleagues from robotics, software, and experimental science backgrounds.
• Ability to model Argonne’s core values of impact, safety, respect, integrity, and teamwork.

Nice To Haves

• Experience integrating sensors with robotic systems, including cameras, tactile sensors, or force and torque sensing.
• Experience with teleoperation hardware and collecting demonstration data for robot learning.
• Familiarity with machine learning frameworks such as PyTorch, and exposure to imitation learning, reinforcement learning, or robotics foundation models.
• Experience contributing to an open-source project.
• Exposure to laboratory automation, scientific instrumentation, or self-driving laboratories.
• Experience with containerized deployment using Docker.

Responsibilities

• Integrate robotic hardware, including robotic arms, mobile manipulators, and humanoid platforms, into autonomous laboratories.
• Bring new robotic platforms from initial hardware setup through operation under laboratory orchestration software.
• Write and extend device modules and REST APIs in MADSci so new platforms can be controlled through the laboratory’s common interface.
• Integrate cameras, tactile sensors, and force sensors with robots.
• Collect robot demonstration data using teleoperation devices.
• Support training and evaluation of policies built on large robotics foundation models.
• Help determine which tasks are appropriate for learned policies versus conventional control.
• Collaborate with staff across robotics, software, and scientific domains.
• Contribute to MADSci, with contributions released publicly.

Benefits

• Comprehensive benefits are part of the total rewards package.

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level