Jobs at Vinci4D.ai

The Strategic Account Executive will drive growth in semiconductor accounts by managing complex sales processes, building relationships, and leading cross-functional teams to deploy Vinci's Physics AI platform.

Build procedural generators that produce parametric, hardware-like geometries and physically plausible simulation setups (materials, boundary conditions, loadings). Implement programmatic CAD workflows, geometry processing and meshing pipelines, measure dataset diversity/coverage, and iterate dataset composition to improve physics ML model performance.

Lead product marketing to translate Vinci's physics and ML capabilities into clear value propositions, messaging, launches, sales enablement, analyst engagement, and thought leadership. Work with engineering, product, ML, and go-to-market teams to craft customer narratives, positioning, and customer stories that resonate with engineers and executives to drive understanding, adoption, and advocacy of novel physics-driven products.

Design, implement, and optimize nonlinear solid-mechanics solvers (geometric and material nonlinearity). Build FEM discretizations, iterative linear solvers and preconditioners, and port/optimize code for GPUs. Contribute to CI/CD, testing, profiling, and collaborate with domain experts to translate models into robust, production-quality implementations.

Design and implement low-latency, scalable runtime solutions to decompose and distribute production physics simulations across multi-GPU and multi-node environments. Focus on parallelism, correctness, performance optimization, validation, CI, and moving prototypes into production while working closely with ML researchers and physicists.

Lead design and implementation of reusable, production-grade simulation libraries and APIs; enforce code quality, testing, and CI/CD; mentor engineers; convert research prototypes into scalable customer-facing features; collaborate with physicists, ML researchers, and computational geometry experts to ensure correctness and performance of simulation and ML systems.

Design and validate runtime evaluation mechanisms to guarantee simulation correctness. Collaborate with ML, solver, physics, and software teams to build data-driven validation, define implementable architectures, run large-scale simulations, and demonstrate solution validity in production environments.

Develop and productionize ML-driven physics simulation capabilities: experiment with temporal propagation, generate and curate training/verification data, iterate prototypes to production, collaborate with physicists, AI researchers, engineers, and customers to scale transient PDE and dynamics modeling.