About the Role

You will own the inference backbone behind QVAC's local AI stack: the C++ systems layer that makes models run fast, reliably, and predictably on real user hardware. The role is centered on engineering quality at runtime level, including startup behavior, memory pressure, throughput/latency balance, and long‑session stability. You will define and evolve the core abstractions that inference features depend on, so new capabilities can be added without sacrificing performance or maintainability. This is a role for someone who enjoys low‑level problem solving, clear technical ownership, and building infrastructure that other teams trust in production. Your work directly enables private, on‑device AI experiences and helps set the technical foundation for QVAC’s next generation of peer‑to‑peer AI products.

Responsibilities

• Work on deploying machine learning models to edge devices using the frameworks: llama.cpp, ggml, onnx
• Colla...