Systems Engineer - Simulation Correctness (Senior)

Responsibilities

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

Requirements

• 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
• Growth & Opportunity
• 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.

Benefits

Additional Information

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.

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Company Intel

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