Lead Data Scientist (Scientific Software Engineer / Computational Scientist) - Only W2Saransh Inc • United States
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Lead Data Scientist (Scientific Software Engineer / Computational Scientist) - Only W2
Saransh Inc
- United States
- United States
Über
Location Mountain View, CA (Hybrid – 3 days a week onsite)
Job Type W2 Contract
Note Only Visa Independent candidates are required (No C2C or Third-party candidates)
Experience Level Lead
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.
Simulation & Generative Modeling Seeking a 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.
What We're Looking For
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
Sedimentology
Tectonics
Geostatistics
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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Sprachkenntnisse
- English
Hinweis für Nutzer
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