Periodic Labs develops AI-driven scientific research platforms that automate laboratory workflows. By creating autonomous laboratories and AI scientists, the company accelerates experimental discovery and reduces human labor. Their technology integrates advanced machine learning models with robotic instrumentation, enabling rapid hypothesis testing and data generation. Periodic Labs distinguishes itself through end-to-end automation, real-time data analytics, and a collaborative culture that attracts top talent in AI and life sciences.
Open Positions
LLM Inference Engineer
Experience with optimizing inference, high-performance model serving frameworks, and distributed inference techniques
CUDA Kernel Engineer
2-10 years of experience in writing and optimizing CUDA kernels, working with Nvidia hardware, and integrating kernels into inference and training frameworks
Automation Engineer
BS in EE/CS/ME/Controls or equivalent; 5+ years in lab automation, integration, or industrial automation software; Strong experience with scripting/programming (Python preferred)
Reliability Engineer
5+ years of experience in reliability, mechanical, or systems engineering, especially related to supporting materials science/solid-state chemistry and/or thin-film processing research equipment
Research Scientist, Materials Characterization
PhD in Physics or Materials Science with 5+ years of experience in physical property characterization
Distributed Training Engineer
Experience with training on clusters with ≥5,000 GPUs, 5D parallel LLM training, and distributed training frameworks
Research Engineer - Posttraining
4-5 requirements, including experience with RL environments, evals, and training datasets
Systems Engineer
BS in Mechanical, Electrical, Systems, or related engineering field, 3+ years commissioning or bringing up complex lab, pilot, or manufacturing equipment, hands-on experience with installation and testing of complex lab equipment
Research Engineer, Lab Automation
PhD in Materials Science or equivalent experience, with a track record of hands-on experimental work, and strong experimental design and data analysis skills
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