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About the job

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

• Collaborate closely with researchers to assist in coding, training and transitioning models from research to production environments

• Integrate AI features into existing products, enriching them with the latest advancements in machine learning

Job requirements

• Excellent programming skills in C++, experience in Javascript is a bonus

• Strong experience with Llama.cpp and ggml inference engines, which facilitates the deployment of models to specific GPU architectures

• Good understanding of deep learning concepts and model architectures

• Experience with transformers, LLMs, Diffusion models

• Demonstrated ability to rapidly assimilate new technologies and techniques

• A degree in Computer Science, AI, Machine Learning, or a related field, complemented by a solid track record in AI R&D

Bonus points if:

• You have experience with Javascript/Typescript

• You understand the difficulties, nuances and importance of p2p technology

• You have experience with any of Vulkan, Metal and OpenCL

• You have productionized models

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