Vinci
Engineering
SystemsEngineer-SimulationCorrectness(Senior)
Neural analysis suggests this role is
optimal for Senior candidates.
“Systems Engineer - Simulation Correctness (Senior) at Vinci. Skills: Simulation correctness, Machine Learning, Physics simulation. Validate simulation systems. Evaluate ML solutions”
What You'll Achieve.
Increase simulation throughput; Expand simulation capabilities; Support global deployment
Industry & Context.
Root cause analysis; Debugging; Troubleshooting
What They're Looking For.
Must Have
Prior experience using or building physics simulators, Experience as a systems engineer, Basic understanding of solver, Working knowledge of ML basics, Understanding of statistics and data science methods, Software engineering fundamentals, Excellent communication skills, Excellent documentation skills
Nice to Have
Worked as a Systems Engineer for a production Software Solution, Leveraged simulation for design or data generation, Experience delivering solutions, Worked on validation solutions for a production ML system
What You'll Do.
Validate simulation systems
Evaluate ML solutions
Evaluate Solver solutions
Build runtime evaluation mechanism
Develop data driven argument
Run thousands of simulations
Find failure in haystack
How You'll Work.
Team & Collaboration
Working with Scientists; Working with Engineers; Interface of teams; Working with Physicists; Working with AI researchers; Working with Software Engineers; Working with Computational Geometry experts; Cross-functional teams
Communication Scope
Documentation; Communication
Full 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 D
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