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.
About
the Role As a Process Development Engineer, you will play a crucial role in designing and optimizing the workflows and tools that enable high-efficiency process technologies, primarily for the semiconductor industry. You will contribute to lab design and expansion, define hardware engineering specs, perform hardware characterization, and troubleshoot complex engineering issues. You will also be hands-on in establishing and standardizing process flows, generating and analyzing data, creating technical documentation, and engaging with customers to ensure alignment between lab capabilities and market needs. In this role, you will experiment, learn, and collaborate daily with internal teams spanning AI/ML, simulation, automation, and physics/chemistry to build scalable solutions for high-value problems.
Responsibilities Process Engineering: Bring structure to unstructured workflows by establishing process control metrics. Conduct complex design of experiments (DOE), define recipe structures from scratch, implement statistical process control (SPC) and fault detection and classification (FDC), and extend these practices across a wide range of equipment. Lab Development: Contribute to the design, construction, and scale-up of an AI-accelerated thin-film and semiconductor processing laboratory, enabling scientists to pursue materials discovery. Customer & Vendor Engagement: Communicate and engage directly with key customers and vendors to resolve highly complex process engineering issues and address customer High Value Problems (HVPs). Build modular test stands or hardware needed for proof-of-concept experiments. Documentation & Data: Create internal and external documentation, strategies, procedures, presentations, and technical reports, and establish standardized data pipelines to feed into AI training infrastructure.