About the role
About Periodic Labs We are an AI + physical sciences lab building state of the art models to make novel scientific discoveries. We are well funded and growing rapidly. Team members are owners who identify and solve problems without boundaries or bureaucracy. We eagerly learn new tools and new science to push forward our mission. What to Expect Join a world-class team of scientists and engineers pushing the boundaries of materials research in a groundbreaking lab where AI and automation unlock discoveries at unprecedented speed and scale. We’re looking for a materials-minded Research Engineer who is excited by the prospect of using lab automation and AI to accelerate the discovery of new materials. You’ll collaborate with our materials scientists to conceptualize new ways to automate scientific processes, and work with mechanical and robotics engineers to prototype and test proof-of-concept systems, and eventually refine them into production-ready tools that can generate reliable data every day.
Responsibilities Translate scientific goals into an automation roadmap: identify high-value targets, outline
benefits and risks, and prioritize what to build next. Plan, run, and analyze proof-of-concept experiments Write clear user
requirements and partner with engineers and vendors to turn them into practical designs and build plans. Co-develop and evaluate prototypes designs—iterate quickly with mechanical, robotics, and controls engineers to develop reliable lab automation systems Define data and metadata needs so automated workflows produce trustworthy, reusable results. Support installation and commissioning; ensure the resulting system fits the lab’s safety, usability, and reliability standards.
Qualifications PhD in Materials Science (or related field) or equivalent experience, with a track record of hands-on experimental work. Demonstrated engineering instincts—custom instruments, automation-focused PhD work, or post-PhD industry experience in automation. Strong experimental design and data analysis (e.g., Python or similar), with a bias toward measurable results. Familiarity with one or more relevant domains: thin films, solid-state synthesis, in-situ characterization, or automated property testing. Clear communicator who can translate between scientists, engineers, and vendor partners.