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 What You Will Do 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. Qualifications 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. Preferred Experience 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. Compensation & Benefits Competitive salary, equity, health benefits, flexible time off, and remote‑friendly policies. Final compensation depends on experience and location. Compensation Range: $165K - $180K #J-18808-Ljbffr