AI Inference Engineer QVAC (100% remote Worldwide)
Tether Operations Limited3 days ago
Remote, WorldwideSenior
Responsibilities
- Deploy machine learning models to edge devices using llama.cpp and ggml.
- Port and enhance inference engines to run efficiently across different hardware and GPU architectures.
- Optimize model startup behavior, memory pressure, throughput, latency, and long-session stability.
- Develop and maintain core C++ runtime abstractions for QVAC’s local AI stack.
- Collaborate with researchers on coding, training, and transitioning models from research to production.
- Integrate AI features into existing products.
Requirements
- Strong programming skills in C++.
- Strong experience with llama.cpp and ggml inference engines.
- Experience with at least one of CUDA, Vulkan, Metal, or OpenCL.
- Good understanding of deep learning concepts and model architectures.
- Experience with transformers, LLMs, and diffusion models.
- Ability to rapidly learn new technologies and techniques.
- A degree in Computer Science, AI, Machine Learning, or a related field, with a solid track record in AI R&D.
- Training or fine-tuning LLMs, productionizing models, research in new model architectures, distributed systems, and JavaScript experience are bonuses.
Benefits
- 100% remote work worldwide.
- Opportunity to build private, on-device AI products without relying on cloud infrastructure.
- Work with a globally distributed team on digital finance, AI, and peer-to-peer technology.
Tech Stack
Categories
About Tether Operations Limited
Tether Operations Limited develops and issues reserve-backed digital tokens used by exchanges, wallets, payment processors, and merchants, most notably the USDT stablecoin available across multiple blockchains. Founded in 2014 and privately held, it earns revenue from token issuance, reserve management, and related services, and has expanded into tokenization, peer-to-peer communications (Keet), and Bitcoin mining. USDT is widely used in crypto trading and cross-border transfers, including in emerging markets.