Backend Infrastructure Engineer

Building the Backbone

Our unified model architecture delivers steady‑state and transient solutions to partial differential equations at production scale. None of that reaches a customer without robust infrastructure underneath it: reproducible builds, automated pipelines, well‑provisioned cloud, and code that holds up under load. This role owns that backbone. You will make our systems fast, dependable, and easy for physicists, researchers, and engineers to build on.

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.

• 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, Num Py, CUDA, GPU infrastructure).
• Experience with containers and orchestration (Docker, Kubernetes) and observability tooling.

• 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.

Competitive salary, equity, health benefits, flexible time off, and remote‑friendly policies. Final compensation depends on experience and location.