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
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