W2 - 15+ Lead Data Scientist - Mountain View, CA (3 days per week from office)

Position Lead Data Scientist 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. Employment type C2C Project duration 9 months with possible extension Location Mountain View, CA Work mode 3 days per week from office Travel No Recruitment process General -> Technical Interview -> Manager Interview -> Client Interview Required start date April 1 or earlier Level Lead level Work authorization statu H1B and TN visa candidates can be considered. Only W2.       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