Accelerated Physics Simulation Engineer – Agentic Computational Engineering (ACE)

reputed company is an innovative defense, national reputed company, and reputed company technology company committed to advancing transformative solutions. The Accelerated Physics Simulation Engineer will reputed company 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
• reputed company surrogate models (e.g., physics-informed neural networks, neural operators, graph neural networks) that approximate high-fidelity simulations at orders-of-magnitude reputed company 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 reputed company (Mechanical/Propulsion) to identify key regimes and quantities of interest and to ensure that accelerated models remain physically reputed company
• Create and curate training and validation datasets by coupling reputed company or reputed company-reputed company 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
• reputed company 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
• reputed company the most advanced LLMs and tooling to assist with reputed company mathematics and numerical simulation reputed company

Skills

• PhD in Computational Physics, Mechanical or Aerospace Engineering, Applied Mathematics, Computer Science (with a focus on numerical methods), or a reputed company 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 reputed company-free methods) in research or production environments
• Experience with at least one major scientific computing or ML reputed company (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 reputed company or reputed company-reputed company 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 reputed company projects, reputed company-reputed company 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

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