Periodic Labs · 2 months ago
Research Engineer, Lab Automation
Periodic Labs is an AI + physical sciences lab focused on making novel scientific discoveries through advanced models. They are seeking a materials-minded Research Engineer to use lab automation and AI to accelerate the discovery of new materials, collaborating with scientists and engineers to develop and refine automation systems.
Artificial Intelligence (AI)Foundational AIGenerative AIMachine Learning
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
Qualification
Required
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
Preferred
Broad general experience with inorganic materials synthesis, characterization, and testing
Experience selecting, operating, and maintaining scientific instruments
Accomplishments recognized in your field
Company
Periodic Labs
Periodic Labs develops artificial intelligence systems that simulate and predict the properties of materials using machine learning.
Funding
Current Stage
Early StageTotal Funding
$300M2025-09-30Seed· $300M
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