---
title: 'Member of Technical Staff - Geometry / Meshing Engineer at Vinci4d'
canonical: 'https://feeny.ai/job/member-of-technical-staff-geometry-meshing-engineer-vinci4d-palo-alto-1jj17ptsvz05'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff - Geometry / Meshing Engineer at Vinci4d

- **Company:** Vinci4d
- **Location:** Palo Alto, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-05-28
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/vinci4d/4b9a1905-815e-4276-a9d5-c97a86ea7beb

## Job description

Vinci4d is building the next-generation copilot for hardware designers and engineers, integrating rich text, image and 3D geometry (STL, OBJ, STEP, etc.) workflows with machine learning, physics modeling, and geometry processing pipelines. Our mission is to dramatically accelerate hardware design through intelligent automation, and we are looking for a strong engineer in geometry and meshing to help us build the core systems.

## ROLE OVERVIEW

As a Geometry / Meshing Engineer, you will help design, implement, and optimize the geometry and mesh-generation systems that underpin our copilot platform. You will work at the intersection of computational geometry, mesh generation (surface and volume), numerical methods, GPU/parallel architectures, and large-scale geometry data (often millions of points/cells). You will collaborate with our ML and physics simulation teams, contribute to system architecture, and help ensure our geometry infrastructure meets the needs of our hardware-design workflows.

This is primarily a geometry role. Meshing experience is welcome but not required. We are also interested in strong engineers from adjacent areas who can grow into meshing, including those with a background in high-performance geometry or graphics, in HPC and parallel computing, or in physics simulation.

## RESPONSIBILITIES

- Develop and optimize algorithms for geometry import, repair, parameterization, mesh refinement/coarsening, adaptation, quality improvement, and mesh-to-simulation interoperability.
- Contribute to geometry processing and mesh generation pipelines (surface/volume, unstructured/structured, quad/hex/tri/tet) suitable for industrial-scale hardware design.
- Work with large geometry/mesh datasets (e.g., hundreds of millions of points/cells), designing for performance, memory efficiency, and GPU/parallel execution.
- Interface with CAD/geometry input (STEP, IGES, B-Rep, etc.), preprocess geometry for meshing, and handle complex topologies, feature preservation, and simulation readiness.
- Collaborate with ML, physics, simulation, and UI/UX teams to define requirements, integrate geometry and mesh generation into the larger system, and deliver high-impact features.

Qualification

- Strong software engineering skills in C++ (modern C++ standards) and/or Python, with solid experience in algorithm design and implementation.
- Demonstrated ability to write high-performance code and to profile and optimize for CPU/GPU performance and memory efficiency.
- Experience working in a relevant technical domain such as computational geometry, graphics, simulation, or another numerically intensive area.
- Strong problem-solving and analytical skills, and the ability to collaborate across disciplines (e.g., simulation, ML, CAD/geometry).

## Nice to Have

- Experience with mesh generation techniques (triangulation, tetrahedral meshing, hex/quad meshing, mesh refinement/coarsening) and quality metrics.
- Experience with GPU / CUDA / OpenCL, high-performance computing (HPC), multi-threading, or distributed computing.
- Experience with CAD import/export (STEP, IGES, B-Rep) and geometry kernels (Parasolid, ACIS, OpenCASCADE).
- Background in physics simulation.
- A degree (BS, MS, or PhD) in Computer Science, Applied Mathematics, Mechanical/Aerospace Engineering, or a related field, or equivalent practical experience.

## About Vinci4d

## Company Overview
- **One-liner**: Vinci4D builds a physics AI foundation model that enables deterministic, solver-accurate simulation at manufacturing resolution—eliminating meshing, approximations, and customer-specific training.
- **Entity Type**: Private (Series A, 2025)
- **Headquarters**: Palo Alto, California, United States
- **Founded**: 2023
- **Founders**: Dr. Hardik Kabaria (CEO) and Dr. Sarah Osentoski (CTO)

## Core Business
- **Primary industry**: Semiconductor design and simulation; AI-powered physics simulation for hardware engineering.
- **Target customers**: Enterprise (semiconductor and systems companies), B2B.
- **Mission or purpose statement**: Build a physics AI foundation model that natively understands the laws of physics, enabling engineers to innovate at speed and scale once unimaginable.

## Products & Services
- **Vinci Platform**: A physics AI foundation model that performs full-manufacturing-resolution simulations (thermal, thermo-mechanical, warpage) out-of-the-box – no meshing, no approximations, no customer data required. Validated against traditional FEA solvers with <2% deviation and up to 1000× speed improvement. Deployed securely behind customer firewalls.

## Market Standing
- **Valuation/Market Cap**: Not disclosed (private company)
- **Key Metric**: Total funding – **$36.5M** (LinkedIn) or **$46M** (conflicting reports per newsroom; includes $36M Series A + $500k seed + possible additional tranche). [getvinci.ai](https://www.getvinci.ai/newsroom/) [linkedin.com](https://www.linkedin.com/company/vinci4d-ai)
- **Notable Investors/Partners**: Khosla Ventures, Eclipse Ventures, Xora. Strategic partnerships with leading semiconductor companies (over half of top 20 semiconductor firms have benchmarked Vinci).
- **Growth Signals**: Employee headcount grew **550% YoY** to 27 people; peer-reviewed validation published at EPTC 2025; 240× to 360× speed improvements demonstrated in customer benchmarks.

## Competitive Advantages
- **Physics-native AI foundation model** that inherently respects governing laws – not a generic LLM.
- **No meshing required** – works directly with native CAD/layout files (OASIS, GDS, IPC-2581) preserving full design fidelity.
- **No customer data needed** – model generalizes out-of-the-box without retraining.
- **Deterministic, solver-accuracy** (<2% deviation from traditional FEA) at 1000× speed, making it production-grade.

## Strategic Focus
- **Current priorities**: Deepen adoption in semiconductor packaging and electronics thermal simulation; expand into adjacent hardware domains (e.g., automotive, aerospace) where full-resolution physics simulation is critical.
- Continue validating through peer-reviewed research and scaling the platform to handle extreme degrees of freedom (100+ million elements).

## Why Work Here
- **Culture**: Founded by AI and simulation experts from Stanford/UC Riverside; team includes world-class simulation engineers and AI researchers (former Carbon, NVIDIA, Zoox engineers).
- **Work environment**: Startup pace with strong research and engineering focus – technical roles dominate (57% of headcount).
- **Funding & stability**: Well-backed by top-tier VCs; runway from $36M+ Series A.
- **Flexibility**: Offices in Palo Alto and Redwood City, CA; not explicitly stated as remote-first, but typical of early-stage deep-tech startups.
- **Engineering culture**: Focus on building a foundation model for physics using Python, PyTorch, CUDA, Kubernetes, Docker, and simulation tools like Ansys Icepak/FloTHERM.

## Sources
1. [getvinci.ai (Home)](https://www.getvinci.ai/)
2. [getvinci.ai (Team)](https://www.getvinci.ai/team/)
3. [getvinci.ai (Newsroom)](https://www.getvinci.ai/newsroom/)
4. [linkedin.com (Company Page)](https://www.linkedin.com/company/vinci4d-ai)
5. [jobs.ashbyhq.com (Careers)](https://jobs.ashbyhq.com/vinci4d)

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