AI Inference Engineer, QVAC at Tether

Role overview

The QVAC inference engineer works on the C++ runtime that runs local AI models without relying on cloud infrastructure. The role improves model loading, memory use and execution across edge hardware. The posting lists Hanoi, Vietnam.

Responsibilities

• Deploy machine-learning models to edge devices with llama.cpp, ggml and relevant GPU frameworks.
• Improve inference speed, memory use and runtime stability across hardware platforms.
• Work with researchers to turn models into production implementations.
• Integrate on-device AI features into existing products.

Qualifications

• Strong C++ programming and practical llama.cpp or ggml inference-engine experience.
• Experience with CUDA, Vulkan, Metal or OpenCL GPU frameworks.
• Understanding of deep-learning models including transformers, LLMs and diffusion systems.
• Computer science, AI or machine-learning degree and evidence of AI research and development.
• Model fine-tuning, production inference, distributed systems or JavaScript are listed as bonus experience.

Technology in this role

• JavaScript: Model fine-tuning, production inference, distributed systems or JavaScript are listed as bonus .
• C++: The QVAC inference engineer works on the C++ runtime that runs local AI models without relying on cloud .
• C: The QVAC inference engineer works on the C++ runtime that runs local AI models without relying on cloud .
• R: • A degree in Computer Science, AI, Machine Learning, or a related field, complemented by a solid track record in AI .
• LLM: • You know how to train/fine-tune a .
• CAN: You will define and evolve the core abstractions that inference features depend on, so new capabilities can be added without sacrificing performance or .
• Vulkan: Experience with CUDA, Vulkan, Metal or OpenCL GPU .
• CUDA: Experience with CUDA, Vulkan, Metal or OpenCL GPU .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This AI Inference Engineer QVAC (100% remote Worldwide) position at Tether is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “The QVAC inference engineer works on the C++ runtime that runs local AI models without relying on cloud infrastructure. The role improves model loading, memory use and execution across edge hardware. The posting lists…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the AI and data area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Deploy machine-learning models to edge devices with llama.cpp, ggml and relevant GPU frameworks.”; “Improve inference speed, memory use and runtime stability across hardware platforms.”; “Work with researchers to turn models into production implementations.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “Strong C++ programming and practical llama.cpp or ggml inference-engine experience.”; “Experience with CUDA, Vulkan, Metal or OpenCL GPU frameworks.”; “Understanding of deep-learning models including transformers, LLMs and diffusion systems.”. Mark each requirement as demonstrated, adjacent experience or a genuine gap, then use a concise example for the first two categories. A strong example explains what you personally owned, how you made trade-offs, how you validated the result and what you learned. If the posting specifies years of experience, language ability, certifications, location or eligibility, address those facts plainly in your CV or application rather than relying on the employer to infer them. Do not claim a qualification from exposure alone. For a requirement you do not meet, describe relevant transferable experience accurately and ask the recruiter whether it is mandatory.

Technology and working evidence. The technology terms associated with this listing are JavaScript, C++. Treat this as a set of posting-derived signals, not a guarantee that every tool is used daily or that the list is complete. For each term relevant to your experience, prepare one concrete example: the system or workflow, the part you owned, the alternatives considered, and how you tested reliability, security, performance or usability as appropriate. If you have not used a named tool, explain the closest related system you have worked with and how you would ramp up; do not relabel that experience as direct production use. If the source describes technologies beyond this list.

About Tether

Tether develops digital-token products, including USDT, alongside projects in peer-to-peer communications and AI. The employer describes a globally distributed remote team.

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