The Rapid Prototyping Laboratory (RPL) in the Data Science and Learning division builds self-driving laboratories, where robots, scientific instruments and AI work together to run experiments with minimal human intervention. We are seeking a Postbachelor Appointee to develop the robotics software that makes these laboratories work, and to bring new robotic platforms into them.

You will integrate robotic hardware into our autonomous laboratories, including robotic arms, mobile manipulators and humanoid platforms such as Dexmate. This work spans the full path from an unfamiliar robot arriving on a loading dock to that robot running experiments under our orchestration software. You will write and extend device modules and REST APIs in MADSci, our open-source laboratory orchestration framework, so that each new platform becomes controllable through the same interface as the rest of the laboratory.

A second part of the role is sensor integration and data collection for robot learning. You will instrument robots with cameras, tactile and force sensors, collect demonstration data using teleoperation devices, and support the training and evaluation of policies built on large robotics foundation models. You will help determine which tasks are good candidates for learned policies and which are better served by conventional control.

Much of this work involves hardware and software that are new to the group, with incomplete vendor documentation and no established integration path. We are looking for someone who treats that as the interesting part of the job. You will work closely with staff across robotics, software and the scientific domains we support, and your contributions to MADSci will be released publicly.

Position Requirements

• Recently completed Bachelor's degree in computer science, robotics, electrical engineering, mechanical engineering or a related field.
• Proficiency in Python, including experience writing code that others 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 a vendor SDK.
• Experience developing or consuming REST APIs.
• Familiarity with version control and collaborative software development using Git.
• Demonstrated ability to bring up unfamiliar hardware from vendor documentation, and to make progress when that documentation is incomplete.
• Ability to communicate clearly with colleagues from robotics, software and experimental science backgrounds.
• Experience integrating sensors with robotic systems, including cameras, tactile sensors or force and torque sensing.
• Experience with teleoperation hardware and with 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.
• Ability to model Argonne's core values of impact, safety, respect, integrity and teamwork.

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Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package.

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As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law.