About Periodic Labs
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.
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 the team scale customer engagements over time
You Will Thrive in This Role If You Have
Strong foundations in numerical simulation of physical systems — whether fluid dynamics, heat transfer, structural mechanics, electromagnetics or related domains — gained through graduate research, industry, or both
Hands-on experience building or running simulations that solve partial differential equations, including comfort with mesh generation, solver tuning, and debugging numerical instabilities
Proficiency in Python for scripting, automation, and scientific computing (NumPy, SciPy, or equivalent)
A process engineering or physics mindset: you understand that simulations are tools for answering real process questions, and you care about getting physically meaningful results, not just running jobs
Strong communication skills and genuine comfort working directly with customer engineering teams — translating between computational models and manufacturing realities
Willingness to spend extended periods on-site with customers, including in Taiwan
A self-starter orientation: you can own a technical problem from problem definition through to a deployed result, with limited hand-holding
Especially Strong Candidates May Also Have
Background in computational fluid dynamics (CFD), including experience with tools such as OpenFOAM, ANSYS Fluent, Star-CCM+, or custom solvers
Graduate-level research experience building simulation software — from scratch or on top of existing frameworks — in domains such as mechanical or chemical engineering, weather modeling, astrophysics, materials processing, or similar
Experience in semiconductor or advanced packaging processes (underfill, flip-chip, wafer bonding, or related)
Familiarity with physics-informed ML, surrogate modeling, or neural operators applied to simulation acceleration
Experience integrating simulation tools into larger software platforms or automated optimization pipelines
Proficiency in Mandarin, which would be a meaningful advantage for on-site collaboration in Taiwan
Some background in a lab or experimental environment, with an appreciation for how simulations relate to physical process data
Mechanics
Minimum education: bachelor’s degree or an equivalent combination of education and training or experience
Location: Our lab is located in Menlo Park and we prefer folks to be located in Menlo Park or San Francisco but can be flexible based on role
Compensation: The annual compensation range for this role - $200,000-$275,000
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process with our legal support.
We’re building a team of the world’s best — the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. If you’re driven to bring AI to life in the physical world and make discoveries that have never been made before, you belong here.