About the RolePeriodic 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 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
MechanicsMinimum 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 - $180,000-$250,000
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process with our legal support.
