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
Join a world-class team of scientists and engineers pushing the boundaries of physical r&d in a groundbreaking lab where AI and automation unlock discoveries at unprecedented speed and scale.
Periodic Labs is seeking an experienced, hands-on Multiphysics Simulation Scientist / Engineer to develop, execute, and integrate high-fidelity physical simulations of our experimental systems, linking materials processes, thermal and mechanical environments, and electrical/magnetic behavior to our AI-driven R&D pipelines.
Responsibilities
Build and apply multiphysics models for a wide range of processes including but not limited to thermal, mechanical, electromagnetic, plasma, fluid flow and/or chemical reaction phenomena.
Interface simulations with orchestration systems and data infrastructure enabling real-time digital twins and AI feedback loops.
Produce diverse simulated datasets for ML training and reinforcement learning environments.
Collaborate with computational, AI, automation, process, and facilities teams to optimize R&D processes.
Qualifications
PhD or MS in Mechanical, Chemical, Materials, or Aerospace Engineering, or a related discipline.
5+ years of hands-on experience with multiphysics modeling tools (e.g., COMSOL, ANSYS, or other finite-element / finite-volume solvers) to solve a wide range of real-world problems in electronics, automotive, aerospace or chemical manufacturing industries.
Deep understanding of coupled physical processes; such as heat transfer, stress, diffusion, plasma/fluid flow, electromagnetism etc.
Strong coding skills in Python
Bonus Qualifications
Experience modeling thin-film deposition related phenomena and chemical reactions.
Familiarity with machine learning approaches relevant to multiphysics models
Comfortable working across disciplines with engineers, scientists, and ML researchers
Multiscale modeling
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