Jobs at Vinci

Own new-logo acquisition and expansion across strategic semiconductor accounts. Lead complex enterprise sales cycles from discovery through multimillion-dollar contract closure, map stakeholders, develop account strategies, and displace legacy simulation tools. Coordinate technical evaluations and deployments with engineering teams, maintain accurate pipeline forecasts, provide market intelligence, and communicate Vinci’s Physics AI value to technical and executive audiences. The hybrid role requires regular customer travel across North America and occasional international travel.

Design and implement production-grade software libraries and APIs for a physics simulation engine. Lead architecture, code quality, testing, CI/CD, modularity, and system correctness. Own core infrastructure, transform research prototypes into customer-facing features, solve complex architectural problems, and mentor engineers and scientists. Collaborate with physicists, AI researchers, software engineers, and computational geometry experts on scalable simulation and machine learning systems.

Design and implement production electromagnetic solvers for signal integrity, power integrity, parasitic extraction, and EM-thermal coupling. Develop frequency-domain, time-domain, and quasi-static methods; S-parameter and touchstone workflows; transmission-line, crosstalk, and eye-diagram analysis; and validation infrastructure against commercial tools. Integrate solver capabilities into a multiphysics platform while maintaining performance, testing, and regression quality.

Develop and optimize nonlinear solid mechanics solvers for large-deformation, material-nonlinear, and thermomechanical simulations. Implement sparse linear algebra, preconditioners, FEM discretizations, and GPU execution using CUDA, HIP, or similar frameworks. Build production-quality C++ and/or Python software, contribute to testing and CI/CD, profile performance, and collaborate with physics and engineering experts. Preferred experience includes semiconductor warpage, matrix-free methods, multigrid, AMR, immersed boundaries, machine learning, and performance analysis tools.

Leads product marketing for Vinci’s physics foundation model, translating complex AI and engineering capabilities into customer value. Responsibilities include product positioning, messaging, launches, customer storytelling, sales enablement, analyst engagement, thought leadership, and market differentiation. The role partners with physics, machine learning, product, and go-to-market teams and communicates with engineers, executives, investors, and technical buyers.

Develop and execute AI-native B2B marketing campaigns for technical enterprise audiences. Identify buying signals, activate engineering teams, expand strategic accounts, and improve engagement through customer intelligence, agentic workflows, segmentation, CRM operations, reporting, and experimentation. Collaborate with Sales, Product Marketing, Application Engineering, and Product to create evidence-driven campaigns and redefine enterprise marketing processes.

Build procedural systems that generate diverse, valid hardware geometries and physically plausible simulation configurations for training and evaluating physics AI models. Develop scripted CAD generators, assign materials and boundary conditions, process geometry and meshes, measure dataset diversity, and connect dataset composition to model performance. The role requires strong Python software engineering, computational geometry expertise, and understanding of physical validity.

Designs, builds, and operates backend services and infrastructure for a production simulation and inference platform. Responsibilities include Terraform-managed cloud environments, CI/CD pipelines, build systems for Python and C++ codebases, infrastructure hardening, and taking research prototypes into reliable customer-facing production systems. The role partners with physicists, AI researchers, software engineers, and computational geometry experts while supporting large-scale scientific and machine learning workloads.

Validate production physics simulation systems by building runtime evaluation mechanisms and data-driven validation approaches. Collaborate with physicists, AI researchers, software engineers, and computational geometry experts to evaluate machine learning and solver solutions. Define implementable architectures, run large-scale simulations, identify failures, and establish CI, regression testing, and validation practices for production software.

Build and simulate thermo-mechanical models for customer hardware designs, evaluating warpage, stress, reliability, nonlinear behavior, and contact interactions. Support product demonstrations, proof-of-concept evaluations, onboarding, training, troubleshooting, and technical workshops. Collaborate with customer engineering, sales, product, and engineering teams to communicate simulation results, identify workflow challenges, improve the product, and represent the company at industry events.

Develop and productionize machine learning applications for physics simulation, including transient modeling, temporal propagation, geometry meshing, and simulation acceleration. Generate and curate training and verification data, prototype and validate new capabilities, and collaborate with physicists, AI researchers, software engineers, and computational geometry experts to deliver customer-ready solutions. Contribute to production data systems, model infrastructure, testing, validation, and software engineering practices.

Own production release lifecycles and deployment decisions while designing and executing system integration and automated tests. Build Python and Playwright test frameworks for web and physics-AI systems, validate numerical outputs against FEA benchmarks, champion test-driven development, coordinate releases, and translate technical changes into customer-facing release notes.