Physics Applications - Researcher

Vinci4D; Palo Alto, Northern (CA, KY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Vinci is building operator intelligence infrastructure for modern hardware programs. You will work with a cross-disciplinary team to advance simulation capabilities, from data generation to production-ready features, engaging with customers to deliver real value.

You will leverage the Vinci framework on diverse applications, exploring temporal propagation, model accuracy, and data-driven validation in a startup setting.

Qualifications

• Have published work leveraging or building a simulation practice.
• MS/MSc with 4+ years experience or PhD with 2+ years experience
• 2+ years using or building physics simulators (FEM, FEA, Molecular Dynamics, FDTD)
• Applied machine learning approaches (Computer Vision, GraphNN, Transformer Architectures)
• Working knowledge of modern ML basics (back prop, loss functions, embeddings, transformers)
• Experience with some modern ML practices (PyTorch, Numpy, Cuda; Training & Evaluation practices)
• Have contributed to a production data processing system.

Responsibilities

• Produce production-ready simulation tooling and workflows.
• Experiment with temporal propagation schemas and both classical and learned approaches.
• Collaborate with physicists, AI researchers, software engineers and geometry experts.
• Generate and curate training and verification data; iterate with customers to harden features.

Skills

Simulation

Applied ML

GraphNN

PyTorch

FEM/FEA

Education

MS/ MSc in related field

PhD with 2+ years

Tools

CUDA

Numpy

Job description

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 into the transient domain, modeling interactions, deformation and dynamics. 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.

In this role you will leverage the Vinci framework to work on a variety of novel applications. Experiment with temporal propagation schemas, both in the classical and learned sense. You will use pre-existing data generation infrastructure to generate and curate training and verification samples. You will take modest iterative steps to prove out new utility and iterate hand in hand with customers to harden features.

You will work with Physicists, AI researchers, Software Engineers and Computational Geometry experts. You will work with a team to carry early prototypes through iteration and hardening all the way to customer use.

What We’re Looking For

Qualifications;

• Have published work leveraging or building a simulation practice.

  • MS/MSc with 4+ years experience or

  • PhD with 2+ years experience

• 2+ years using or building physics simulators

  • FEM, FEA, Molecular Dynamics, FDTD

• Applied machine learning approaches

  • Computer Vision, GraphNN, Transformer Architectures

• Working knowledge of modern ML basics

  • back prop, loss functions, generators, embeddings, transformer models

• Experience with some modern ML practices

  • PyTorch, Numpy, Cuda

  • Training & Evaluation practices

• Have contributed to a production data processing system.

We are very excited to talk with you if you have

• Applied ML to the problem of

• Meshing geometry

• Accelerating FEM or DFT simulations

• 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

• Software engineering fundamentals

  • Comfortable meeting software design standards to get code into a production environment.

• Capable of leveraging pre-existing infrastructure and “closing the gap” on occasion

• Strong CI, regression testing, and validation discipline

• Comfort evolving core model infrastructure

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.