What are the responsibilities and job description for the Lead Data Scientist (Scientific Software Engineer / Computational Scientist) - Only W2 position at Saransh Inc?
Role: Lead Data Scientist (Scientific Software Engineer / Computational Scientist)
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
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.
Core Competencies
What We're Looking For:
Mathematical maturity in:
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
- 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.
The Role
This is an implementation-heavy position bridging procedural physics and generative ML.
Core Competencies
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
- 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
- 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
Mathematical maturity in:
- Structural modeling
- Sedimentology
- Tectonics
- Geostatistics
- MS/PhD in Computer Science, Applied Mathematics, Computational Physics, or equivalent
- Portfolio/GitHub demonstrating procedural world-building, physics engines, or scientific simulators