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

Tether; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Job summary

Tether Ltd. is seeking a senior AI researcher to drive architecture development for cutting-edge LLM and multi-modal models. You will optimize pre-training procedures, improve cross-modal alignment, and push the boundaries of scalable AI on massive GPU clusters.

You will apply deep expertise in transformer models, leverage PyTorch and Hugging Face, and contribute to data strategies, experiments, and system scalability to advance state-of-the-art capabilities in digital finance AI.

Qualifications

• PhD in NLP/ML or related field with strong AI R&D track record.
• Hands-on with large-scale LLM/Multi-Modal pre-training on thousands of GPUs.
• Deep knowledge of transformer and non-transformer models for efficiency.
• Proficiency in PyTorch and Hugging Face for model development and deployment.

Responsibilities

• Lead large-scale pre-training for LLMs and multi-modal models on distributed GPU clusters.
• Design and prototype novel architectures and alignment strategies to improve model performance.
• Source and curate large-scale multi-modal datasets; build robust data pipelines.
• Plan and execute experiments; analyze results; iterate training methods for efficiency.

Skills

LLM & multi-modal

PyTorch

Hugging Face

Distributed training

Research publications

Education

PhD in NLP / ML

CS degree

Tools

NVIDIA GPUs

Distributed training frameworks

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 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.

Innovate with Tether

Tether Finance:

Our innovative product suite features the world’s most trusted stablecoin, USDT, relied upon by hundreds of millions worldwide, alongside pioneering digital asset tokenization services.

Tether Power:

Driving sustainable growth, our energy solutions optimize excess power for Bitcoin mining using eco-friendly practices in state-of-the-art, geo-diverse facilities.

Tether Data:

Fueling breakthroughs in AI and peer-to-peer technology, we reduce infrastructure costs and enhance global communications with cutting-edge solutions like KEET, our flagship app that redefines secure and private data sharing.

Tether Education

Democratizing access to top-tier digital learning, we empower individuals to thrive in the digital and gig economies, driving global growth and opportunity.

Tether Evolution

At the intersection of technology and human potential, we are pushing the boundaries of what is possible, crafting a future where innovation and human capabilities merge in powerful, unprecedented ways.

Why Join Us?

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.

Are you ready to be part of the future?

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.

You will have a deep expertise in Large Language Model (LLM) and Multi-Modal architectures, a strong grasp of pre-training optimization, and a hands-on, research-driven approach. Your mission is to explore and implement novel techniques and algorithms that lead to groundbreaking advancements: multi-modal data curation and alignment, strengthening baselines, and identifying and resolving existing pre-training bottlenecks to push the limits of cross-modal AI performance.

Responsibilities

• Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi-nodes & 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.

• 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 good 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 pretraining, and deployment.