---
title: 'Forward Deployed Engineer, Physics & Simulation at Periodic Labs'
canonical: 'https://feeny.ai/job/forward-deployed-engineer-physics-simulation-periodic-labs-menlo-park-6adt32pby47s'
type: 'job'
last_seen: '2026-09-07'
---

# Forward Deployed Engineer, Physics & Simulation at Periodic Labs

- **Company:** Periodic Labs
- **Location:** Menlo Park, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-06-03
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/periodic-labs/1bab7e17-20fd-4b82-a152-98b96375a626

## 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 (Soon: San Francisco, too) + frequent travel to Taiwan

Compensation: $200,000-$275,000 + equity

Visa sponsorship: Yes, we sponsor visas.

## About Periodic Labs

## Company Overview
- **One-liner**: Periodic Labs is building AI scientists and autonomous laboratories to accelerate scientific discovery in the physical sciences, starting with materials design and semiconductor research.
- **Entity Type**: Private (seed-stage, $300M total funding raised in 2025)
- **Headquarters**: San Francisco, California, USA (primary) and Menlo Park, California, USA (dual locations) [cbinsights.com](https://www.cbinsights.com/company/periodic-labs), [linkedin.com](https://www.linkedin.com/company/periodic-labs)
- **Founded**: 2025 [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
- **Founders**: Ekin Dogus Cubuk (Co-Founder) and other undisclosed founding team members with backgrounds at OpenAI, DeepMind, and Google [linkedin.com](https://www.linkedin.com/company/periodic-labs), [periodic.com](https://periodic.com/)

## Core Business
- **Primary industry/industries**: Physical sciences R&D, artificial intelligence, autonomous laboratory systems
- **Target customers**: B2B – enterprises in semiconductor manufacturing, materials science, energy, and other deep-tech sectors requiring experimental research
- **Mission or purpose statement**: “Our goal is to create an AI scientist” that can autonomously hypothesize, run experiments, and learn from results to discover new materials and accelerate technological progress [periodic.com](https://periodic.com/)

## Products & Services
- **AI Scientist**: A suite of AI models trained to generate hypotheses, design experiments, and interpret results across physical science domains.
- **Autonomous Laboratories**: Fully automated, high-throughput experimental platforms that produce large volumes of high-quality data (including negative results) to train AI models. [periodic.com](https://periodic.com/)
- **Custom Agents for Industry**: Tailored AI agents for engineers and researchers, e.g., helping a semiconductor manufacturer analyze heat dissipation data to accelerate iteration. [periodic.com](https://periodic.com/)

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private seed-stage company)
- **Key Metric**: Total funding raised – $300M in a single seed round (2025) [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
- **Notable Investors/Partners**: a16z, Felicis, DST, NVentures (NVIDIA’s venture arm), Accel, Emerson Collective, Fellows Fund, Coatue; individual backers include Jeff Bezos, Elad Gil, Eric Schmidt, Jeff Dean. Also collaborating with academic advisors from Stanford, Northwestern, and more. [periodic.com](https://periodic.com/), [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
- **Growth Signals**: 40 employees as of mid-2026, with monthly headcount growth of +19.6% and 25 active job postings (+47.1% quarterly growth) [linkedin.com](https://www.linkedin.com/company/periodic-labs)

## Competitive Advantages
- **Proprietary data generation**: Autonomous labs produce massive, unique datasets (including valuable negative results) that do not exist in public literature, creating a data moat.
- **Founding team pedigree**: Core team co-created ChatGPT, DeepMind’s GNoME, OpenAI’s Operator (Agent), MatterGen, and scaled autonomous physics labs – rare concentration of AI + science talent. [periodic.com](https://periodic.com/)
- **End-to-end integration**: AI models, simulation, and physical experimentation are all in-house, enabling rapid closed-loop learning.

## Strategic Focus
- **Scale autonomous labs**: Expand capacity to generate experimental data across multiple physical science domains.
- **Target high-impact problems**: Superconductor discovery, semiconductor heat dissipation, and other materials challenges that can unlock Moore’s Law, advanced energy grids, and space travel. [periodic.com](https://periodic.com/)
- **Develop the first generation of AI scientists**: Train models that can operate independently of human input across the entire scientific method.

## Why Work Here
- **Culture**: “From bits to atoms” – a deep-tech startup blending frontier AI with hands-on lab work. Emphasis on rapid iteration and real-world impact.
- **Work policy**: In-office preferred; primary offices in Menlo Park and San Francisco, CA. Most roles are on-site. [builtin.com](https://builtin.com/company/periodic-labs)
- **Notable perks**: Work alongside world-class AI researchers and experimental scientists; access to cutting-edge automated lab equipment; early-stage equity and high-growth trajectory.
- **Engineering culture**: 34% of employees are in technical roles; talent sourced from OpenAI, Google DeepMind, Meta, xAI, and Stanford – strong peer learning environment. [linkedin.com](https://www.linkedin.com/company/periodic-labs)

## Sources
1. [periodic.com](https://periodic.com/)
2. [builtin.com](https://builtin.com/company/periodic-labs)
3. [cbinsights.com](https://www.cbinsights.com/company/periodic-labs)
4. [linkedin.com](https://www.linkedin.com/company/periodic-labs)
5. [jobs.ashbyhq.com/periodic-labs](https://jobs.ashbyhq.com/periodic-labs)

## Other roles at Periodic Labs

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- [Process Technician, Thin Films](https://feeny.ai/job/process-technician-thin-films-periodic-labs-menlo-park-v0m9kw9t9506) — Menlo Park, CA
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