AI Inference Engineer QVAC (100% remote Worldwide)

Colombia·ColombiaRemote

Other

0 views0 saves0 applied

Apply Now

Quick Summary

Key Responsibilities

llama.cpp, ggml Collaborate closely with researchers to assist in coding, training and transitioning models from research to production environments Integrate AI features into existing products,

Technical Tools

Other

At Tether, we’re not just building products, we’re pioneering a global financial revolution. Our cutting-edge solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing the power of blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, all at a fraction of the cost. Transparency is the bedrock of everything we do, ensuring trust in every transaction.

What We Offer

~1 min read

Our team is a global talent powerhouse, working remotely from every corner of the world. If you’re passionate about making a mark in the fintech space, this is your opportunity to collaborate with some of the brightest minds, pushing boundaries and setting new standards. We’ve grown fast, stayed lean, and secured our place as a leader in the industry.

If you have excellent English communication skills and are ready to contribute to the most innovative platform on the planet, Tether is the place for you.

About the Role

~2 min read

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.

You'll work on the C++ layer that powers local AI, porting and enhancing inference engines like llama.cpp or similar, to run efficiently on edge devices. Your focus is on the runtime: making models load faster, run leaner, and perform well across different hardware. You'll ensure that the inference layer is stable, optimized, and ready for integration with the rest of the stack.

This role is for engineers who want to work close to the metal, enabling private and fast on-device AI without relying on cloud infrastructure.

Responsibilities

~1 min read

→
Work on deploying machine learning models to edge devices using the frameworks: llama.cpp, 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

→
Strong programming skills in C++
→
Strong experience with Llama.cpp and ggml inference engines, which facilitates the deployment of models to specific GPU architectures
→
Experience with any GPU framework between Cuda, Vulkan, Metal, OpenCL
→
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