Voyager Technologies, Inc.
Accelerated Physics Simulation Engineer – Agentic Computational Engineering (ACE)
About this role
Voyager seeks a computational engineer to build fast, differentiable physics solvers and machine learning surrogates that enable AI agents to optimize hardware designs across millions of iterations. You'll combine numerical methods, GPU acceleration, and physics-informed neural networks to compress hardware development cycles from years to days.
What you'll do
- Design and implement accelerated physics solvers (CFD, thermal, structural, plasma) for agentic optimization loops
- Develop surrogate models using physics-informed neural networks, neural operators, and graph neural networks
- Integrate solvers and surrogates into the ACE platform as callable tools for AI agents
- Collaborate with applications leads to validate models against high-fidelity solvers and test data
- Curate training datasets and establish validation protocols for surrogate accuracy
- Optimize kernel performance on GPUs to achieve millisecond-scale execution
What they're looking for
- Numerical methods and PDE solving (CFD, thermal, structural analysis)
- GPU computing and CUDA/HIP optimization
- Physics-informed neural networks (PINNs) and surrogate modeling
- Machine learning (neural operators, graph neural networks)
- Differentiable programming and automatic differentiation
- Python, C++, or similar systems languages
- Validation and uncertainty quantification
- High-performance computing and computational efficiency
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Voyager Technologies, Inc.
Voyager Technologies builds advanced systems and technologies for defense and aerospace applications, including missile defense programs, rocket motor systems, and solid propellant technologies for space and national security. The company is hiring systems engineers, test engineers, and project engineers at multiple levels to lead technical development, oversee complex programs, and drive innovation across their defense and space portfolios.
View all jobs at Voyager Technologies, Inc.Likely interview questions
- Describe your experience building and optimizing physics solvers on GPUs—what performance improvements have you achieved?
- How have you validated surrogate physics models against high-fidelity simulations or experimental data?