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 DoYour 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 ForQualifications;
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.
Have experience delivering solutions when needed
Have worked on validation solutions for a production ML system
Software engineering fundamentals
Understanding of CI, regression testing, and validation discipline
Excellent communication and documentation skills
Comfortable running thousands of simulations and finding a needle in the haystack failure.
Capable of defining an architecture with sufficient detail an Engineer could implement it with few open questions.
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 to be the first Systems Engineer in a burgeoning space and to build a practice and team around you. You will work with a premiere physics simulation tool—a proven foundation model capable of billion-voxel inference—that is scaling deployment across Tier-1 ecosystems. Our ambition is for this technology to become the default operator intelligence layer for hardware companies.
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.
Skills Required
- Prior experience using or building physics simulators, including FEM, FEA, Molecular Dynamics, or FDTD
- Experience as a systems engineer in a production environment
- Experience collaborating with scientists and engineers
- Basic understanding of solver mechanisms, numerical optimization, convergence criteria, and dampening approaches
- Working knowledge of machine learning fundamentals, including backpropagation, loss functions, generators, embeddings, and transformer models
- Understanding of statistics and data science methods, including confidence intervals, uncertainty quantification, and Bayesian methods
- Software engineering fundamentals, including CI, regression testing, and validation discipline
- Excellent communication and documentation skills
- Ability to run thousands of simulations and identify rare failures
- Ability to define detailed architectures that engineers can implement
- Systems engineering experience in robotics, chip manufacturing, or aerospace
- Experience leveraging simulation for design or data generation
- Experience delivering production solutions
- Experience developing validation solutions for production machine learning systems
What We Do
Vinci develops physics-based AI software for hardware design and engineering simulation. Its foundation model combines AI acceleration with verified physics and finite-element solver accuracy to produce full-resolution, manufacturing-scale simulations without meshing, approximations, or customer training data. The platform is designed to help semiconductor and systems companies evaluate complex physical designs faster, including nanometer-scale components, while maintaining accurate, production-ready results in seconds instead of days.
