Job description

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 We’re using AI-driven simulation to solve hard physical process optimization problems in advanced manufacturing. As a forward deployed engineer focused on physics and simulation, you will be the technical backbone of our most demanding customer engagements – spending significant time on-site, embedding directly with customer teams, and owning simulation workflows end-to-end. You’ll work with our modeling and ML teams to build and calibrate physics-based simulations, turn customer process knowledge into computational models, and drive recipe optimization with direct feedback loops to production. This is a hands-on, high-ownership role at the frontier of AI for physical science. This role requires travel to and extended time on-site in Taiwan. WHAT YOU’LL DO - Own the simulation workflow end-to-end for customer engagements, from model setup and calibration through optimization and results interpretation - Run, debug and modify physics-based simulations of complex physical processes in diverse domains, such as microfluidics, charge transport and structural deformation - Work on-site with customer engineering teams on-site to understand process constraints, interpret simulation results into real process improvements - Write tools, skills and agents to reliably drive end-to-end LLM-based simulation workflows, including experimental validation, parameter fitting and recipe optimization - Build and extend simulation tooling in Python – job submission, parameter sweeps, output parsing, integration - Feed domain insights back to the research and product teams, shaping the next version our platform YOU WILL THRIVE IN THIS ROLE IF YOU HAVE - A strong foundation in numerical simulation of continuum systems – fluid dynamics, heat transfer, structural mechanics, electromagnetics, or similar – gained through graduate research, industry, or both - Hands-on experience solving partial differential equations numerically, including mesh generation, solver tuning, and debugging numerical instabilities - Solid Python skills for scripting and scientific computing (NumPy, SciPy, or similar) - A process engineer’s instinct: you treat simulations as tools for answering real process questions, not just jobs to run - Strong communication skills and genuine comfort working directly with customer engineers - Willingness to spend extended periods on-site in Taiwan - A self-starter mindset: you can take a technical problem from definition to deployed result without much hand-holding ESPECIALLY STRONG CANDIDATES MAY ALSO HAVE - CFD background, including tools like OpenFOAM, ANSYS Fluent, Star-CCM+, or custom solvers - Grad-level research experience building simulation software in domains like mechanical or chemical engineering, weather modeling, astrophysics, or materials processing - Familiar with semiconductor manufacturing processes - 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 - Mandarin proficiency for on-site collaboration in Taiwan - Lab or experimental background, with an appreciation for how simulation connects to physical data MECHANICS Minimum education: Bachelor’s degree or similar experience Location: Menlo Park, CA + frequent travel to Taiwan Compensation: $250,000-$350,000 + equity Visa sponsorship: Yes, we sponsor visas.

Requirements

Must-haves

This role requires travel to and extended time on-site in Taiwan.
Own the simulation workflow end-to-end for customer engagements, from model setup and calibration through optimization and results interpretation
Run, debug and modify physics-based simulations of complex physical processes in diverse domains, such as microfluidics, charge transport and structural deformation
Work on-site with customer engineering teams on-site to understand process constraints, interpret simulation results into real process improvements
Write tools, skills and agents to reliably drive end-to-end LLM-based simulation workflows, including experimental validation, parameter fitting and recipe optimization
Build and extend simulation tooling in Python – job submission, parameter sweeps, output parsing, integration
Feed domain insights back to the research and product teams, shaping the next version our platform
YOU WILL THRIVE IN THIS ROLE IF YOU HAVE
A strong foundation in numerical simulation of continuum systems – fluid dynamics, heat transfer, structural mechanics, electromagnetics, or similar – gained through graduate research, industry, or both
Hands-on experience solving partial differential equations numerically, including mesh generation, solver tuning, and debugging numerical instabilities
Solid Python skills for scripting and scientific computing (NumPy, SciPy, or similar)
A process engineer’s instinct: you treat simulations as tools for answering real process questions, not just jobs to run
Strong communication skills and genuine comfort working directly with customer engineers
Willingness to spend extended periods on-site in Taiwan
A self-starter mindset: you can take a technical problem from definition to deployed result without much hand-holding
CFD background, including tools like OpenFOAM, ANSYS Fluent, Star-CCM+, or custom solvers
Grad-level research experience building simulation software in domains like mechanical or chemical engineering, weather modeling, astrophysics, or materials processing
Familiar with semiconductor manufacturing processes
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
Mandarin proficiency for on-site collaboration in Taiwan
Lab or experimental background, with an appreciation for how simulation connects to physical data
Minimum education: Bachelor’s degree or similar experience
Location: Menlo Park, CA + frequent travel to Taiwan

Nice-to-haves

None flagged.

Application prep

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aimlllmpython

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