Backend Infrastructure Engineer

Your north star will be production and delivering value to our customers.

• Design, build, and operate the backend services and infrastructure that power Vinci’s simulation and inference platform.
• Own infrastructure-as-code with Terraform across our cloud environments, provisioning and managing services reliably and repeatably.
• Build and harden CI/CD pipelines (Jenkins or equivalent) so the team can ship with confidence and strong regression coverage.
• Maintain and evolve our build systems (CMake, pip/uv) across large Python and C++ codebases.
• Take early prototypes through iteration and hardening all the way to customer use, in modest iterative steps.
• “Close the gap” on pre-existing infrastructure where needed, and evolve core platform components as the product grows.
• Partner closely with Physicists, AI researchers, Software Engineers, and Computational Geometry experts to turn research into dependable systems., 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.

Physics, Semiconductors, Simulation, Ecosystems, Differential Equations, Cloud Computing, Python Programming/Scripting Language, CMake, C++ Programming Language, Prototyping, * 8+ years of professional software engineering experience, with a strong focus on backend and infrastructure.

• Infrastructure & cloud: hands-on Terraform experience and production work on at least one major cloud (GCP, AWS, Azure, or similar).
• CI/CD: built and maintained pipelines with Jenkins or an equivalent CI system.
• Build systems: comfortable with build tooling such as CMake and Python packaging via pip/uv.
• Languages: proficient in Python, with working familiarity in C++.
• Scale: comfortable navigating and contributing to large, multi-language codebases.
• Have contributed to a production data processing or platform system., * Experience taking an early-stage prototype to a production environment, at a startup or national lab.
• Exposure to frontend development.
• Familiarity with 3D rendering or visualization technologies.
• Experience supporting ML or scientific-computing workloads (PyTorch, NumPy, CUDA, GPU infrastructure).
• Experience with containers and orchestration (Docker, Kubernetes) and observability tooling.

Engineering Expectations

• Strong software engineering fundamentals; comfortable meeting software design standards to get code into a production environment.
• Capable of leveraging pre-existing infrastructure and “closing the gap” on occasion.
• Strong CI, regression testing, and validation discipline.
• Comfort evolving core platform and model infrastructure.

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. To get there, we need the backend and infrastructure that lets our research move to production reliably, repeatably, and at scale., 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 build the infrastructure that lets our unified model architecture-currently running billion-voxel inference-scale and expand into the transient domain, a key frontier in modeling interactions, deformation, and dynamics.