Systems Engineer - Simulation Correctness

Getvinci; Palo Alto (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Getvinci in Palo Alto is looking for a Systems Engineer to validate simulation systems and enhance software solutions leveraging Machine Learning. You will collaborate with physicists, AI researchers, and software engineers to deliver high-value results while being a key player in a growing startup.

This role requires experience in building physics simulators and systems engineering, providing an opportunity to influence the next generation of hardware programs.

Qualifications

• Experience as a systems engineer in a production environment.
• Basic understanding of solver mechanisms and algorithms.
• Comfortable working in collaborative settings with diverse teams.

Responsibilities

• Use and evaluate cutting edge solutions developed by ML and Solver teams.
• Build runtime evaluation mechanisms for simulation.
• Collaborate with physicists, AI researchers, and software engineers.

Skills

Prior experience using or building physics simulators

Working knowledge of ML basics

Understanding of statistics and data science methods

Tools

FEM/FEA tools

Molecular Dynamics

Numerical Optimization

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.

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.
• Have experience delivering solutions when needed
• Have worked on validation solutions for a production ML system

Engineering Expectations

• 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.

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 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.