AI Inference Engineer QVAC (100% remote Worldwide)

## Join Tether and Shape the Future of Digital Finance

At Tether, we're pioneering a global financial revolution through cutting-edge blockchain solutions. We're looking for a talented engineer to own the inference backbone behind QVAC's local AI stack.

## About the Role

You will own the C++ systems layer that makes machine learning models run fast, reliably, and predictably on real user hardware. This role is centered on engineering quality at runtime level, including startup behavior, memory pressure, throughput/latency balance, and long-session stability. You'll define and evolve core abstractions that inference features depend on, enabling new capabilities 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 frameworks like llama.cpp and ggml
• 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
• Port and enhance inference engines to run efficiently on edge devices
• Ensure the inference layer is stable, optimized, and ready for integration with the rest of the stack

## Requirements

• Excellent programming skills in C++; experience in Javascript is a bonus
• Strong experience with Llama.cpp and ggml inference engines
• Good understanding of deep learning concepts and model architectures
• Experience with transformers, LLMs, and Diffusion models
• Demonstrated ability to rapidly assimilate new technologies and techniques
• Degree in Computer Science, AI, Machine Learning, or a related field, complemented by solid track record in AI R&D