Systems Engineer - Simulation Correctness

About the role

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.

Design the Software that Designs Hardware

Integrating Machine Learning with Classic Numerical approaches results in a solution that is better than the sum of its parts. This method reduces the complexity of physics simulations, making them easier to setup, run and evaluate quickly. This combination of ease of use, speed and accuracy is the core of our value proposition to customers.

WHAT YOU WILL DO

Your north star will be the guaranteed (empirical) validation of simulation systems.

In this role you will use and evaluate the cutting edge solutions developed by our Machine Learning and Solver teams. Ensure that our customers receive the highest value results by building a runtime evaluation mechanism. Develop a compelling data driven argument for this mechanism. Work with software engineers to implement your designs and demonstrate validity.

You will sit at the interface of teams of Physicists, AI researchers, Software Engineers and Computational Geometry experts. You are comfortable working with deep technical experts and bringing your own expertise to bear.

WHAT WE’RE LOOKING FOR

Qualifications;

- Prior experience using or building physics simulators - FEM, FEA, Molecular Dynamics, FDTD

- Experience as a systems engineer in a production environment - working with Scientists and Engineers in a collaborative setting

- Basic understanding of solver mechanisms; - Numerical Optimization, Convergence Criteria, Dampening approaches

- Working knowledge of ML basics - back prop, loss functions, generators, embeddings, transformer models

- Understanding of statistics and data science methods - Confidence intervals, uncertainty quantification, Bayes method

We are very excited to talk with you if you have

- Worked as a Systems Engineer for a production Software Solution in any of; - Robotics, Chip Manufacturing, Aerospace

- Have leveraged simulation for design or data generation purposes.