Forward Deployed Engineer
US$250,000 – US$350,000 / yr base salary
Posted 3 Jun 2026
Advertised as “Forward Deployed Engineer, Physics & Simulation”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
- Visa sponsorship
- Offered
| Base salary | US$250,000 – US$350,000 / yr |
|---|
Study levelBachelor's
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
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Own the simulation workflow end-to-end for customer engagements, from model setup and calibration through optimization and results interpretation
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Run, debug and modify physics-based simulations of complex physical processes in diverse domains, such as microfluidics, charge transport and structural deformation
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Work on-site with customer engineering teams on-site to understand process constraints, interpret simulation results into real process improvements
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Write tools, skills and agents to reliably drive end-to-end LLM-based simulation workflows, including experimental validation, parameter fitting and recipe optimization
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Build and extend simulation tooling in Python – job submission, parameter sweeps, output parsing, integration
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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
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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
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Hands-on experience solving partial differential equations numerically, including mesh generation, solver tuning, and debugging numerical instabilities