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. Simulation Runtime Mapping a computational problem to a runtime environment is the engine of our product. Making our simulations run fast across heterogeneous compute platforms while retaining accuracy is at the center of our value proposition to customers. Delivering involves overcoming many challenges; efficient data sharing or customizing inference and math to bespoke runtime hardware. The value: making Vinci Simulations run effectively regardless of the hardware: from a single desktop to multi-gpu clusters that span global data center sites. This kind of compute platform flexibility means simulations are easier to run and complete faster when hardware permits. The ability to losslessly divvy up simulations across large scale compute resources will unlock new utility for our customers and power larger more useful applications. What You Will Do Your north star will be parallelism and correctness. In this role you will design and implement low-latency, scalable solutions to decompose and distribute our production simulations across the most challenging computational boundaries; Multi-GPU machines Multi-Node clusters Networked nodes What We’re Looking For Being successful in this role requires a deep understanding of scientific computing methods, boundary decomposition problems, and parallel computing.
Qualifications; Experience working on High Performance Computing runtime applications Experience with any of highly parallel computing frameworks; MPI, MPICH, ZMQ, OpenMP Experience with GPU Programming; Cuda, ROCM, Triton Have contributed to a production data processing system. Familiarity with Statistical validation methods Outlier detection, Bayes method, convergence criterion for nonlinear solvers Familiarity with ML basics back prop, loss functions, generators, embeddings, transformer models We are very excited to talk with you if you have Worked on highly performant deployed inference environments Have shipped HPC library components Experience going from early stage prototype moving to a production environment At a Startup or National Lab Experience with highly parallel ML training frameworks such as Ray Engineering Expectations Software engineering fundamentals Comfortable meeting software design standards to get code into a production environment. A practical approach to prototyping necessary components that are currently missing. Strong CI, regression testing, and validation discipline Comfort learning and evolving model deployment & runtime infrastructure 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 for technical and professional growth, as you will define a foundational abstraction layer early in the company's trajectory.
The team is small, friendly, and accessible. You will be empowered to "own and architect large pieces of the system" alongside a team of Physicists, AI researchers, Software Engineers, and Computational Geometry experts. This includes greenfield opportunities to expand Vinci’s core capabilities. 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.
