You will architect and implement the core physics engine that powers the company. Your primary technical challenge is robustness without human intervention . You must build systems that can accept arbitrary, "dirty" geometry from a customer and return a converged simulation result without a user ever clicking "repair" or setting a mesh size.

Key Responsibilities:

• Automated Mesh Generation: Develop and refine algorithms to handle non-manifold, non-watertight, or complex assembly geometry. You will implement logic that automatically determines mesh density, boundary layers, and refinement zones based on feature detection (e.g., identifying heat sinks vs. chassis walls).
• Headless Solver Orchestration: Build the backend logic to drive OpenFOAM and other solvers programmatically. This includes automatic convergence monitoring, divergence recovery strategies, and dynamic timestep adjustment.
• Geometric Kernel Development: Work directly with B-Rep and mesh data structures (using tools like OCCT, Gmsh, or custom C++ implementations) to perform boolean operations, volume extraction, and surface repair at scale.
• Validation & Benchmarking: Create the case suites that verify our automated results against industry standards (Ansys/Fluent) to ensure physics fidelity.

Who We Are Looking For

We are looking for an engineer who understands the mathematical fundamentals of simulation, not just how to use a GUI. You should be able to look at a diverged residual plot and identify whether the issue is poor mesh quality, aggressive under-relaxation, or an ill-posed boundary condition.

Qualifications:

• Background: MS or PhD in Engineering Physics, Applied Mathematics, Computer Science, or Mechanical Engineering.
• Core Tech: Expert proficiency in Python (for orchestration) and C++ (for core numerics/meshing).
• Simulation Experience: Deep experience with OpenFOAM , Ansys Fluent , or Star-CCM+ . You understand the internal file structures (dictionaries, schemes) and have likely written custom scripts or user-defined functions (UDFs).
• Geometry: Familiarity with computational geometry concepts (NURBS, tessellation, isosurface extraction). Experience with kernels like Parasolid , ACIS , or OpenCASCADE is a strong differentiator.

Preferred Experience:

• Experience writing custom meshers or modifying open-source meshing libraries (Netgen, Gmsh).
• Background in High-Performance Computing (HPC), specifically optimizing solver performance on AWS/EC2 or Dockerized environments.
• Previous work on "verticalized" simulation tools (e.g., automated wind tunnel apps, internal tools for turbomachinery).