Lead Data Scientist
Mountain View, CA - Hybrid
Contract
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:
1. 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
2. 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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