Accelerated Physics Simulation Engineer – Agentic Computational Engineering (ACE)

Voyager Technologies is an innovative defense, national security, and space technology company committed to advancing transformative solutions. The Accelerated Physics Simulation Engineer will develop high-fidelity physics simulation capabilities to optimize hardware designs using AI agents, working with numerical methods, GPU computing, and machine learning.

Responsibilities

• Design and implement fast physics solvers (e.g., CFD, thermal, structural, plasma) suitable for use inside agentic optimization loops
• Develop surrogate models (e.g., physics-informed neural networks, neural operators, graph neural networks) that approximate high-fidelity simulations at orders-of-magnitude lower cost
• Integrate accelerated solvers and surrogates into the ACE platform so AI agents can call them as tools during design and optimization
• Work with the ACE Applications Lead (Mechanical/Propulsion) to identify key regimes and quantities of interest and to ensure that accelerated models remain physically credible
• Create and curate training and validation datasets by coupling commercial or open-source solvers (e.g., Ansys, COMSOL, Star-CCM+, OpenFOAM) with automated parameter sweeps
• Profile and optimize GPU kernels and numerical pipelines, targeting large speedups over baseline codes while preserving required accuracy
• Develop test harnesses, benchmarks, and diagnostics that track accuracy, stability, and performance of accelerated models over time
• Use LLMs to accelerate boilerplate coding, experiment scripting, and documentation so you can focus on core numerical and physical insights
• Leverage the most advanced LLMs and tooling to assist with complex mathematics and numerical simulation generation

Skills

• PhD in Computational Physics, Mechanical or Aerospace Engineering, Applied Mathematics, Computer Science (with a focus on numerical methods), or a related field; or Master's degree + 3 years of highly relevant experience
• 0–3 years of post-PhD industry, startup, or postdoctoral experience (or 3–6 years total experience working in computational science/engineering)
• Hands-on experience implementing numerical methods for PDEs (e.g., FEM, FVM, FDM, particle or mesh-free methods) in research or production environments
• Experience with at least one major scientific computing or ML framework (e.g., JAX, PyTorch, TensorFlow) and one GPU or performance-oriented technology (e.g., CUDA, PhysicsNEMO, etc)
• Demonstrated experience speeding up simulations or building surrogate models for physics problems, with quantitative before/after results
• Demonstrated 'AI-first' workflow: you use LLMs to help generate, refactor, and test code so you can spend more time on modeling and physics
• Experience with CFD, structural mechanics, heat transfer, or plasma physics as applied to aerospace or propulsion systems
• Experience with electrical, power, and electromagnetic simulations as applied to PCB or RF systems
• Prior work on physics-informed neural networks (PINNs), neural operators (FNO, UNO, etc.), or other ML-based surrogates for physical systems
• Experience coupling commercial or open-source solvers (e.g., Ansys, COMSOL, Star-CCM+, OpenFOAM) with custom automation or optimization code
• Familiarity with differentiable programming and adjoint methods for design optimization
• A track record of side projects, open-source contributions, or competition results that demonstrate deep enthusiasm for computational physics and performance engineering

Benefits

• Competitive salary
• Discretionary annual bonus plan
• Paid time off (PTO)
• Comprehensive health benefit package
• Retirement savings
• Wellness program
• Various other benefits

Company Overview