AI Engineer (Edge inference)

At Tether, we're committed to making advanced AI technologies more accessible. Thanks to its investment in AI infrastructure, starting from Northern Data, Tether is now in a prime position to tackle ambitious AI projects. Our goal is to build the next generation of AI models, leading innovation in AI, through an accessible, transparent and privacy preserving approach.

The role involves building AI solutions across the spectrum from large-scale models designed for advanced applications to smaller, highly performant models tailored for efficiency on edge devices such as mobile phones and laptops.

Our dynamic team operates entirely remotely, uniting talent from every corner of the globe. Our journey has been marked by rapid growth and efficient operations, firmly establishing us as pioneers within the industry. Join us in building AI models and solutions that not only compete with but exceed the capabilities of current leaders, driving both technological advancement and broad accessibility.

What You'll Do:

• Work on deploying machine learning models to edge devices using frameworks such as TVM, MLC, and IREE (MLIR).
• 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 Python, C and C++.
• Experience with platforms such as TVM, MLC, and IREE (MLIR), which facilitate the deployment of models to specific GPU architectures.
• Experience in NLP, computer vision, TensorFlow, PyTorch, JAX and CUDA toolkit.
• Experience with different aspects of Large Language Models (LLMs), such as fine-tuning techniques to tailor models to specific tasks and prompt engineering.
• Extensive experience in training models using multi-GPU setups.
• 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.