Systems Engineer - Simulation Correctness

The MissionAt 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 dataRunning billion-voxel inference in productionTier-1 semiconductor and hardware customersOperating across multiple physical scales and operator regimesWe are scaling deployment at industrial magnitude:Increase simulation throughput by two orders of magnitudeExpand simulation capabilities to maximize utility and domain coverageSupport global, multi-entity deployment across Tier-1 ecosystemsOur ambition is to become the default operator intelligence layer that hardware companies run on.Design the Software that Designs HardwareIntegrating 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, FDTDExperience as a systems engineer in a production environment working with Scientists and Engineers in a collaborative settingBasic understanding of solver mechanisms; Numerical Optimization, Convergence Criteria, Dampening approachesWorking knowledge of ML basics back prop, loss functions, generators, embeddings, transformer modelsUnderstanding of statistics and data science methods Confidence intervals, uncertainty quantification, Bayes methodWe are very excited to talk with you if you haveWorked as a Systems Engineer for a production Software Solution in any of; Robotics, Chip Manufacturing, AerospaceHave leveraged simulation for design or data generation purposes.Have experience delivering solutions when neededHave worked on validation solutions for a production ML systemEngineering ExpectationsSoftware engineering fundamentals Understanding of CI, regression testing, and validation disciplineExcellent communication and documentation skillsComfortable 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 VinciJoin 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 & ImpactVinci 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 & OpportunityThis 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.LeadershipYou 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.