Lead Data Scientist (Scientific Software Engineer / Computational Scientist) - Only W2

Mountain View, CA, US • Posted 12 hours ago • Updated 1 hour ago

Contract W2

On-site

Fitment

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Job Details

Skills

C++
PyTorch
ML Frameworks
Data Science
Linear Algebra
Simulation
Generative ML
Generative Modeling
voxel engines
Terrain synthesis
Python (NumPy/SciPy/CuPy)
Geostatistics
3D Mathematics
CUDA/OpenMP
AI-assisted coding
vector calculus

Summary

Role: Lead Data Scientist (Scientific Software Engineer / Computational Scientist)

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

Job Type: W2 Contract

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:

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
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

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.

Dice Id: 91091604
Position Id: 2026-3369
Posted 12 hours ago

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Lead Data Scientist (Scientific Software Engineer / Computational Scientist) - Only W2

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Job Details

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