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

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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