---
title: 'Software Engineer – Nonlinear Solid Mechanics & High-Performance Computing at Vinci4d'
canonical: 'https://feeny.ai/job/software-engineer-nonlinear-solid-mechanics-high-performance-computing-vinci4d-b10dp27jf00z'
type: 'job'
last_seen: '2026-09-08'
---

# Software Engineer – Nonlinear Solid Mechanics & High-Performance Computing at Vinci4d

- **Company:** Vinci4d
- **Location:** Palo Alto, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-06-24
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/vinci4d/8a0659bf-5cf0-4e90-b06a-a60c9f755f65

## Job description

## About Us

At Vinci4d, we are building the next generation of simulation software for thermal, fluid flow, and structural mechanics applications — the kind of tools that change how engineers design products, from the first mesh to the final answer. We are a small, technically deep team that moves fast, ships real software, and takes on hard problems that matter. If you want your work to be foundational to a platform used by engineers worldwide, this is the place.

## The Role

We are looking for a software engineer who lives at the intersection of computational solid mechanics, numerical methods, and high-performance computing. You will design, implement, and tune solvers for geometric and material nonlinearity in solid mechanics — think large-deformation, contact, and history-dependent material response — that run at scale on modern hardware. You will write production-quality code, contribute to our CI/CD infrastructure, and collaborate closely with a multi-disciplinary team of physicists, engineers, and software developers.

This is not a "maintain the existing stack" role. You will be building things that don't exist yet, solving problems that require both rigorous mathematical thinking and solid engineering instincts.

## What You Will Work On

- Develop and tune nonlinear solvers for solid mechanics, handling both geometric nonlinearity (large deformation, finite strain) and material nonlinearity (plasticity, viscoelasticity, temperature-dependent and history-dependent constitutive models)
- Build and optimize the underlying linear algebra: iterative linear solvers and preconditioners for the large sparse systems arising at each Newton iteration
- Port and optimize these solvers for GPU execution using CUDA, HIP, or equivalent frameworks, with a focus on memory bandwidth, occupancy, and scalability
- Implement FEM discretizations for structural and thermomechanical field solves, with attention to robustness and convergence under stiff, ill-conditioned, and near-singular conditions
- Contribute to a robust software engineering foundation: version control discipline, automated testing, CI/CD pipelines, and code review practices
- Collaborate with domain experts to translate physical models and mathematical formulations into correct, efficient implementations
- Profile and benchmark solver performance; identify and eliminate bottlenecks

## What We Are Looking For

Technical Skills

- Hands-on experience developing solvers for geometric and material nonlinearity in solid mechanics — large-deformation kinematics, nonlinear constitutive models, and the Newton-type schemes that drive them to convergence
- Strong foundation in the finite element method (FEM) for solid and structural mechanics
- Deep familiarity with iterative linear solvers (e.g., Krylov methods) and preconditioning techniques for large, sparse systems, with hands-on experience implementing these inside a nonlinear solver
- Proven GPU programming experience (CUDA, HIP, SYCL, or similar) with a track record of getting real performance out of hardware
- Proficiency in C++ and/or Python; comfort working in performance-critical codebases
- Strong software engineering practices: Git workflows, code review, automated testing (unit, integration, regression), and CI/CD pipelines

## Experience

- 3–6 years of industry or research experience in a relevant field (computational mechanics, scientific computing, computational physics, numerical simulation, or HPC)
- A portfolio of work — open source contributions, published code, or shipped products — that demonstrates the above

Soft Skills

- A genuine collaborator: you learn from teammates as readily as you help them
- Able to communicate technical depth clearly to people from different disciplines — physicists, mechanical engineers, product managers
- Comfortable with ambiguity and excited by the challenges that come with building something new
- Self-directed and ownership-oriented: you drive your work to completion without needing to be managed closely

## Nice to Have

- Experience with warpage and residual-stress problems in semiconductor manufacturing (e.g., packaging, die/substrate stacks, thermomechanical deformation)
- Familiarity with matrix-free methods for nonlinear and linear operator application
- Experience with geometric multigrid approaches as solvers or preconditioners
- Background in adaptive mesh refinement (AMR)
- Familiarity with embedded geometry or immersed boundary methods for solid mechanics
- Experience applying machine learning to solid mechanics problems (surrogates, constitutive modeling, solver acceleration)
- Experience with performance profiling tools (Nsight, VTune, Roofline analysis)

## Why Vinci4d

- Work on genuinely hard technical problems with real engineering impact
- Join a small team where your contributions are visible and your voice is heard
- Competitive compensation with equity participation
- Flexible work environment
- The satisfaction of building something from the ground up — and the opportunity to help define what it becomes

## 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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