Backend Infrastructure Engineer
Vinci AI
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.
Qualifications
- 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
- 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
- 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.
- 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.
- 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.
Vacancy posted 4 days ago
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