About the role
About Periodic Labs The most important scientific discoveries of our time won’t happen in a traditional lab. We’re an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what’s scientifically possible.
About
the Role Periodic Labs is deploying AI-driven simulation to solve some of the hardest physical process optimization problems in advanced manufacturing. As a Forward Deployed Engineer focused on physics and simulation, you will be the technical engine behind our most demanding customer engagements — spending significant time on-site, embedding directly with customer teams, and owning the end-to-end simulation workflow that makes our platform work in the real world. You will work alongside our internal modeling and ML teams to build, calibrate, and iterate on physics-based simulations, translate customer process knowledge into computational models, and drive iterative recipe optimization with direct feedback loops to production. This is a hands-on, high-ownership role at the frontier of AI for physical science. Willingness to travel to and spend extended time on-site in Taiwan is required. What You’ll Do Own the simulation workflow end-to-end for customer engagements — from model setup and calibration to iterative recipe optimization and results interpretation Build, run, and debug physics-based simulations of complex physical processes, including multiphase flow, capillary dynamics, viscosity evolution, and curing behavior Collaborate directly with customer engineering teams on-site to understand process constraints, interpret simulation outputs, and translate findings into actionable process improvements Partner with Periodic’s internal ML and RL teams to couple simulation outputs with LLM-driven recipe generation, closing the loop between physics modeling and automated process optimization Develop and extend simulation tooling in Python, including scripting for job submission, parameter sweeps, output parsing, and integration with our Onnes platform Iterate rapidly on model fidelity, meshing strategies, and solver configurations to balance accuracy and computational cost for real-world deployment constraints Surface domain insights back to the research and product teams, directly shaping the next generation of our simulation and AI platform Contribute to documentation, runbooks, and process guides that help