Hybrid AI Engineer – Generative Geometry for Hardware Design

at Vinci4D.ai

• Job title: AI Engineer – Generative Geometry for Hardware Design
• Job type: Full Time
• Experience level: Mid level, Senior
• Salary: $180,000 - $220,000 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, Python, PyTorch, Tensorflow
• Location requirements: Hybrid, Palo Alto, United States

About the role

• AI Engineer developing generative geometry models to enhance hardware design workflows. Collaborating with domain experts and utilizing advanced deep learning techniques for product development.

Responsibilities

• Design conditional generative models for 3D geometry tailored to hardware design workflows, including mesh-based, parametric (e.g., CAD) and implicit representations
• Develop models that generate geometry conditioned on constraints, partial designs, simulation outcomes, or functional requirements.
• Support inverse design tasks where the model proposes viable geometries given desired performance or physical behavior
• Implement cutting-edge generative architectures for 3D data such as:
• - Diffusion models for point clouds, voxel grids, or triangle meshes
• - Neural implicit representations (SDFs, DeepSDF, NeRF variants for shape modeling)
• - Transformer or autoregressive models for topological and geometric sequence modeling
• - CAD-aware generation pipelines (sketch-based or parametric component generators)
• Develop pipelines for geometry-aware learning and generation combining:
• - Mesh and geometry processing (remeshing, simplification, subdivision)
• - Differentiable simulation or physics-informed learning components
• - Conditioning on design constraints, performance targets, or class-specific priors
• Collaborate with domain experts in physics, geometry, and simulation to:
• - Integrate physical principles and simulation feedback into the generation loop
• - Ensure designs meet functional, physical, and manufacturability requirements
• - Translate domain knowledge into data priors, architectural biases, or constraints
• Design experiments and benchmarks to evaluate generation quality such as:
• - Geometry fidelity and resolution
• - Physical plausibility and constraint satisfaction
• - Generalization to novel design tasks or unseen part types
• Build product-facing generative tools, including:
• - Auto-complete or correction of partial designs
• - LLM to CAD generation
• - Proposal of high-quality geometry variants from a design prompt
• - Design-space exploration tools guided by downstream simulation outcomes
• Own projects end-to-end: rapidly prototype models, test ideas, gather feedback and contribute to production deployment in collaboration with cross-functional teams.

Requirements

• 4+ years of experience developing and shipping products
• Strong background in deep learning, especially applied to 3D or spatial data.
• Hands-on experience with mesh generation, implicit surfaces, or neural fields (e.g., NeRF, SDF, DeepSDF, Occupancy Networks).
• Experience with the related technologies, libraries, and languages: Python, C++, PyTorch (3D)/TensorFlow/JAX; plus to have GPU programming
• Experience with diffusion models for 3D generation
• Startup experience is a strong advantage.
• Understanding of geometry representations (mesh, voxel, point cloud, NURBS, parametric surfaces).
• Familiarity with 3D geometry processing, including mesh handling, surface reconstruction, spatial data structures, and basic topology, to support effective 3D model manipulation and analysis