# Accelerated Physics Simulation Engineer – Agentic Computational Engineering (ACE)

Hiring organization: [Voyager](https://career.thegoodapps.co/organizations/voyager)

Canonical page: https://career.thegoodapps.co/jobs/44ef12c8-a8a9-4351-994c-18f5aa8673f8

Listed on Voyager's own careers site. Applications go to them directly.

- Employment type: full time
- Seniority: Junior
- Location: Washington, DC
- Salary: 165000 – 250000 USD per year

## Summary

This role develops fast physics simulation engines and AI-accelerated surrogates that let autonomous agents optimize hardware designs across millions of iterations. It suits computational scientists or engineers who thrive at the intersection of numerical methods, GPU computing, and machine learning, and who want to compress years-long engineering cycles into days.

_Our summary, not Voyager's wording._

## Skills named

ANSYS, Computational Fluid Dynamics (CFD), COMSOL Multiphysics, CUDA, JAX, OpenFoam, PyTorch, TensorFlow

## Required

- PhD in Computational Physics, Mechanical or Aerospace Engineering, Applied Mathematics, Computer Science (numerical methods focus), or related field; or Master's degree with 3+ years relevant experience
- 0–3 years post-PhD industry or postdoctoral experience (or 3–6 years total computational science/engineering experience)
- Hands-on implementation of numerical methods for PDEs (FEM, FVM, FDM, or particle/mesh-free methods)
- Experience with at least one major scientific computing or ML framework (JAX, PyTorch, TensorFlow)
- Experience with at least one GPU or performance-oriented technology (CUDA, PhysicsNEMO, etc.)
- Demonstrated experience accelerating simulations or building physics surrogate models with quantitative results
- AI-first workflow using LLMs to generate, refactor, and test code
- U.S. citizen or eligible for required export authorization

## Nice to have

- CFD, structural mechanics, heat transfer, or plasma physics experience in aerospace or propulsion
- Electrical, power, and electromagnetic simulation experience for PCB or RF systems
- Prior work on PINNs, neural operators (FNO, UNO), or ML-based physics surrogates
- Experience coupling commercial or open-source solvers with custom automation code
- Familiarity with differentiable programming and adjoint methods
- Track record of side projects, open-source contributions, or competition results in computational physics

Apply on Voyager's site: https://job-boards.greenhouse.io/voyagertechnologiesinc/jobs/4079284009