Periodic Labs

Forward Deployed Engineer, Physics & Simulation

Menlo Park

H1B sponsorship available$180k-$250kDetected 49 days ago

PythonMachine LearningNumPyLLMsProcess ImprovementANSYSCFDResearchCommunicationCollaboration

About the role

Periodic Labs is deploying AI-driven simulation to solve some of the hardest physical process optimization problems in advanced manufacturing.
This is a hands-on, high-ownership role at the frontier of AI for physical science.

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
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.
Hands-on experience building or running simulations that solve partial differential equations, including comfort with mesh generation, solver tuning, and debugging numerical instabilities
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
We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond.
Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible.

Background in computational fluid dynamics (CFD), including experience with tools such as OpenFOAM, ANSYS Fluent, Star-CCM+, or custom solvers
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 Python for scripting, automation, and scientific computing (NumPy, SciPy, or equivalent)

Minimum education: bachelor's degree or an equivalent combination of education and training or experience

Willingness to spend extended periods on-site with customers, including in Taiwan

Yes, we sponsor visas and will do everything we can to assist in this process with our legal support.

This listing is sourced directly from Periodic Labs's careers page and normalized into a canonical job model.