AI Research Engineer

Tether Operations Limited; City of Westminster; Remote; GBP 80,000 - 100,000; Full time

Job summary

A pioneering fintech company in the digital finance sector is seeking an AI model team member. You will drive innovations in AI architecture development while enhancing the performance capabilities of various models. Ideal candidates will have a PhD in a related field and hands-on experience with large-scale model training. Join a global team dedicated to pushing the boundaries of technology and innovation in fintech.

Qualifications

• Degree in Computer Science or related fields required.
• Strong record in AI R&D is essential.
• Hands-on experience with large-scale LLM training on distributed GPU servers preferred.

Responsibilities

• Conduct pre-training of AI models on large, distributed servers.
• Design, prototype, and scale innovative architectures.
• Execute experiments, analyze results, and refine methodologies.
• Debug and improve model efficiency and performance.
• Advance training systems for scalability on target platforms.

Skills

Expertise in LLM architectures

Experience with PyTorch

Experience with Hugging Face

Knowledge of transformer architectures

Education

PhD in NLP, Machine Learning, or similar

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 solutions empower businesses—from exchanges and wallets to payment processors and ATMs—to seamlessly integrate reserve-backed tokens across blockchains. By harnessing blockchain technology, Tether enables you to store, send, and receive digital tokens instantly, securely, and globally, at a fraction of the cost. Transparency underpins our operations, ensuring trust in every transaction.

Innovate with Tether

• Tether Finance: Our product suite features the world's most trusted stablecoin, USDT, relied upon by hundreds of millions, alongside digital asset tokenization services.
• Tether Power: Driving sustainable growth by optimizing excess power for Bitcoin mining with eco-friendly practices in geo-diverse facilities.
• Tether Data: Fueling AI and peer-to-peer technology breakthroughs, reducing infrastructure costs, and enhancing global communications with solutions like KEET, our flagship app for secure, private data sharing.
• Tether Education: Democratizing access to top-tier digital learning to empower individuals in digital and gig economies.
• Tether Evolution: Merging technology and human potential to push the boundaries of innovation.

Why Join Us?

Our global team works remotely from around the world. If you're passionate about fintech and have excellent English communication skills, this is your chance to collaborate with top minds, push boundaries, and set new standards. We've grown fast, stayed lean, and secured our leadership position. Join us to contribute to the most innovative platform on the planet.

About the job

As a member of the AI model team, you'll drive innovation in architecture development for models of various scales, including small, large, and multi-modal systems. Your work will enhance intelligence, efficiency, and capabilities in AI. You should have deep expertise in LLM architectures, pre-training optimization, and a research-driven approach. Your mission includes exploring and implementing novel techniques to advance AI performance, including data curation, improving baselines, and resolving pre-training bottlenecks.

Responsibilities

• Conduct pre-training of AI models on large, distributed servers with thousands of NVIDIA GPUs.
• Design, prototype, and scale innovative architectures to improve model intelligence.
• Execute experiments, analyze results, and refine methodologies.
• Debug and improve model efficiency and computational performance.
• Advance training systems for scalability and efficiency on target platforms.

Requirements include a degree in Computer Science or related fields, ideally a PhD in NLP, Machine Learning, or similar, with a strong record in AI R&D. Hands-on experience with large-scale LLM training on distributed GPU servers, familiarity with training frameworks and libraries like PyTorch and Hugging Face, and deep knowledge of transformer architectures are essential.