Lead Data Scientist; Scientific Software Engineer​/Computational Scientist - W2

Role

Lead Data Scientist (Scientific Software Engineer / Computational Scientist)

Mountain View, CA (Hybrid – 3 days a week onsite)

W2 Contract

Only Visa Independent candidates are required (No C2C or Third-party candidates)

Lead

• Python (Num Py/Sci Py/CuPy)
• C++
• Py Torch
• Geostatistics
• 3D Mathematics
• CUDA/OpenMP
• AI-assisted coding

• 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.

Seeking a deep expertise in scientific computing, procedural generation, or computational physics to build the core algorithms for our 3D subsurface modeling engine.

This is an implementation-heavy position bridging procedural physics and generative ML.

• 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

• 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 Py Torch
  • Example:
    Generate realistic fracture networks via 3D generative models
  • Example:
    Apply neural style transfer to map sedimentary textures onto volumetric frameworks

• Languages:
  
  Expert Python (Num Py/Sci Py/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

• Structural modeling
• Sedimentology
• Tectonics
• Geostatistics

• MS/PhD in Computer Science, Applied Mathematics, Computational Physics, or equivalent
• Portfolio/Git Hub demonstrating procedural world-building, physics engines, or scientific simulators