Backend Infrastructure Engineer

About Us

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
• Serving Tier‑1 semiconductor and hardware customers
• Operating across multiple physical scales and operator regimes
• Increasing simulation throughput by two orders of magnitude
• Expanding simulation capabilities to maximize utility and domain coverage
• Supporting global, multi‑entity deployment across Tier‑1 ecosystems

• 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.
• Hands‑on Terraform experience and production work on at least one major cloud (GCP, AWS, Azure, or similar).
• Built and maintained CI/CD pipelines with Jenkins or an equivalent CI system.
• Comfort with build tooling such as CMake and Python packaging via pip/uv.
• Proficient in Python, with working familiarity in C++.
• Comfort navigating and contributing to large, multi‑language codebases.
• Has 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.

Compensation Range: $165K - $180K