| Main skills | Python (NumPy/SciPy/CuPy), C++, PyTorch, Geostatistics, 3D Mathematics, CUDA/OpenMP, AI-assisted coding |
| Short overview | Scientific Software Engineer or Computational Scientist with a niche background in scientific simulation, procedural generation, or computational physics. This is an implementation-heavy role requiring a developer who can translate complex mathematical logic and generative ML models into performant code to solve high-dimensional geometric problems. |
| Description | Scientific Software Engineer / Computational Scientist Simulation & Generative Modeling We're seeking a Simulation Engineer with deep expertise in scientific computing, procedural generation, or computational physics to build the core algorithms for our 3D subsurface modeling engine. The Role This is an implementation-heavy position bridging procedural physics and generative ML. You'll translate complex mathematical logic and latent-space models into performant code, solving high-dimensional geometric problems at scale. What We're Looking For Core Competencies: - Procedural Generation: Terrain synthesis, voxel engines, noise-driven systems
- Scientific Computing: CFD, FEA, multi-physics solvers
- Computational Geometry: 3D mesh processing, volumetric data structures, spatial partitioning
Key Responsibilities: - Algorithmic Implementation - Design memory-efficient algorithms for massive 3D voxel arrays and sparse data structures; implement deterministic and stochastic geometric rules
- Example: Build C++/Python kernels using 3D Perlin/Simplex noise and vector fields to simulate braided river systems
- Example: Implement Boolean CSG algorithms for volumetric injections of igneous bodies
- Generative ML Engineering - Architect and train models (GANs, Diffusion) for high-resolution 3D spatial data using PyTorch
- Example: Generate realistic fracture networks via 3D generative models
- Example: Apply neural style transfer to map sedimentary textures onto volumetric frameworks
Required Technical Skills - Languages: Expert Python (NumPy/SciPy/CuPy); proficient C++ for performance kernels
- Mathematics: Linear algebra, vector calculus, coordinate transformations
- ML Frameworks: PyTorch (generative AI, computer vision)
- Performance: CUDA/OpenMP; parallel computing experience
- Workflow: AI-assisted coding for rapid prototyping and testing
Domain Knowledge Mathematical maturity in: - Structural modeling (Boolean operations, volumetric intersections)
- Sedimentology (layer stacking, erosion, flow simulation)
- Tectonics (displacement fields, kinematic transformations)
- Geostatistics (particle systems, stochastic models)
Ideal Background - MS/PhD in Computer Science, Applied Mathematics, Computational Physics, or equivalent
- Portfolio/GitHub demonstrating procedural world-building, physics engines, or scientific simulators
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