AI Research Engineer (Pre-training - LLM & Multi-Modal)

Tether.io; España; On-site; EUR 90,000 - 160,000; Full time

Job summary

Tether is seeking a senior AI researcher to lead architecture development for scalable pre‑training of LLMs and multi‑modal models. You will push the boundaries of distribution, efficiency, and tokenization, collaborating across teams to improve model intelligence and performance.

Join a pioneering platform advancing digital finance and AI capabilities, with opportunities to shape cutting‑edge systems and contribute to state‑of‑the‑art research efforts in a fast‑growing environment.

Qualifications

• A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D with publications in A* conferences.
• Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training runs on large, distributed servers equipped with thousands of NVIDIA GPUs, ensuring scalability and impactful advancements in model performance.
• Familiarity and practical experience with large-scale, distributed training frameworks, libraries and tools.
• Deep knowledge of state-of-the-art transformer and non-transformer modifications aimed at enhancing intelligence, efficiency and scalability.
• Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pre-training, and deployment.

Responsibilities

• Large‑Scale Pre‑Training: Conduct foundational pre‑training for LLMs and Multi‑Modal models on large, distributed servers equipped with multi‑nodes and thousands of NVIDIA GPUs.
• Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross‑modal alignment layers to enhance model intelligence and multi‑modal understanding.
• Data Strategy: Source, filter, and curate massive‑scale textual and multi‑modal datasets, establishing robust data pipelines for efficient pre‑training.
• Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency.
• Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi‑modal alignment stability during long training runs.
• System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms.

Skills

LLM pre-training

Distributed training

PyTorch

Hugging Face

Model architecture design

AI R&D

Education

PhD in NLP or ML

CS degree

Tools

NVIDIA GPUs

Distributed training libraries

Job description

Join Tether and Shape the Future of Digital Finance

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, 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.

Tether’s portfolio includes our trusted stablecoin USDT, tokenization services, sustainable energy solutions, AI‑driven data platforms, educational initiatives, and continuous evolution of technology.

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 Job

As a member of the AI model team, you will drive innovation in architecture development for cutting‑edge models of various scales, including small, large, and multi‑modal systems. Your work will enhance intelligence, improve efficiency, and introduce new capabilities to advance the field.

Responsibilities

• Large‑Scale Pre‑Training: Conduct foundational pre‑training for LLMs and Multi‑Modal models on large, distributed servers equipped with multi‑nodes and thousands of NVIDIA GPUs.
• Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross‑modal alignment layers to enhance model intelligence and multi‑modal understanding.
• Data Strategy: Source, filter, and curate massive‑scale textual and multi‑modal datasets, establishing robust data pipelines for efficient pre‑training.
• Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency.
• Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi‑modal alignment stability during long training runs.
• System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms.

Qualifications

• A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D with publications in A* conferences.
• Hands‑on experience contributing to large‑scale LLM or Multi‑Modal pre‑training runs on large, distributed servers equipped with thousands of NVIDIA GPUs, ensuring scalability and impactful advancements in model performance.
• Familiarity and practical experience with large‑scale, distributed training frameworks, libraries and tools.
• Deep knowledge of state‑of‑the‑art transformer and non‑transformer modifications aimed at enhancing intelligence, efficiency and scalability.
• Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pre‑training, and deployment.