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Salary
$210k – $285k per year
Location
Hybrid (Palo Alto, United States)
Seniority
Staff · 8+ years exp
Employment
Full-Time

Confirmed on the employer's own hiring board on Nov 4, 2026. First seen by Alion on Jun 10, 2026. Getvinci scores A on the Alion truth index.

Overview
Company
Impact
Profile match
Run full-resolution simulations in minutes. Vinci’s foundation model for physics unites AI acceleration with verified solvers for as-built accuracy.

The Mission

At Vinci, we are building the operator intelligence infrastructure that modern hardware programs rely on daily. We have already proven that a single foundation model works out of the box across physics on realistic production workloads.

  • Trained on PetaBytes of structured physics data

  • Running billion-voxel inference in production

  • Tier-1 semiconductor and hardware customers

  • Operating across multiple physical scales and operator regimes

We are scaling deployment at industrial magnitude:

  • Increase simulation throughput by two orders of magnitude

  • Expand simulation capabilities to maximize utility and domain coverage

  • Support global, multi-entity deployment across Tier-1 ecosystems

Our ambition is to become the default operator intelligence layer that hardware companies run on.

Solving Across Space and Time

Our proven unified model architecture allows users to rapidly obtain steady state solutions of various partial differential equations. We are expanding this capability to support new physics, new geometries. Beyond that we are building out transient solutions, modeling interactions, deformation and dynamics. A core challenge as we scale out support is designing simple and clean interfaces that turn portions of the codebase into a clean library, ensuring they are correct and accurately reflect the underlying physics being modeled. These are promising applications where Vinci’s approach can not only reduce the compute load but also achieve greater accuracy.

What You Will Do

Your north star will be production and delivering value to our customers while establishing and maintaining the technical integrity of our codebase.

In this role you will define and implement high-quality, reusable software libraries for our core simulation engine. You will drive code quality, testability, and architectural standards across the team, ensuring our production system scales gracefully and remains maintainable. This includes designing interfaces that are easy to access and correctly reflect the physics they are modeling. You will take ownership of critical system components, mentor junior engineers, and guide the team in transforming research prototypes into hardened, customer-facing features.

You will work with Physicists, AI researchers, Software Engineers and Computational Geometry experts. You will collaborate closely with this team to enforce software engineering best practices throughout the full development lifecycle, from ideation to deployment.

What We’re Looking For

Qualifications:

  • 8+ years of experience in high-quality software development, with significant experience designing and building production-grade systems.

  • Prior experience with scientific computing or physics simulators (FEM, FEA, Molecular Dynamics, FDTD), or large-scale machine learning systems.

    • STEM MSc, PhD preferred but not required.

  • Demonstrated ability to lead technical initiatives focused on code health, modularity, and system correctness.

  • Expertise in building robust, tested, and maintainable software libraries and APIs.

  • Strong proficiency in modern software development practices, including system design, agentic coding, testing frameworks, and continuous integration/delivery (CI/CD).

  • Have contributed to a production data processing system.

We are very excited to talk with you if you have

  • Experience building and maintaining core ML, data infrastructure, or numerical computing software (e.g., PyTorch, Numpy, Cuda, distributed systems).

  • Experience going from early stage prototype moving to a production environment

    • At a Startup or National Lab

  • Have leveraged simulation for design or data generation purposes.

Engineering Expectations

  • Architectural Leadership: Define and uphold rigorous software design standards to ensure the code base remains clean, modular, and scalable in a production environment.

  • Code Health and Testability: Drive strong CI, comprehensive regression testing, and validation discipline across all components.

  • Technical Ownership: Capable of independently solving complex architectural problems and taking ownership of core model infrastructure evolution.

  • Mentorship: Mentor scientists & engineers on best practices, performance optimization, and system design.

Why Vinci

Join a rare early-stage startup that has successfully moved a foundational product from research to real-world, production environments, already serving Tier-1 semiconductor and hardware customers.

Our Mission & Impact

Vinci is building the operator intelligence infrastructure that modern hardware programs rely on daily. We are scaling our solution to accelerate design validation from hours to seconds. You will contribute to expanding our unified model architecture, which currently runs billion-voxel inference, into the transient domain-a key frontier in modeling interactions, deformation, and dynamics. Our ambition is to become the default operator intelligence layer for hardware companies.

Growth & Opportunity

This is a unique opportunity for technical and professional growth, as you will define a foundational abstraction layer early in the company's trajectory. The team is small, friendly, and accessible. You will be empowered to "own and architect large pieces of the system" alongside a team of Physicists, AI researchers, Software Engineers, and Computational Geometry experts. This includes greenfield opportunities to expand Vinci’s core capabilities.

Leadership

You will work with spectacular technical leaders like CTO Sarah Osentoski and CEO Hardik Kabaria, whose vision is to greatly accelerate physics simulations with ML while retaining solver grade accuracy.

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