AI Research Engineer (Pre-training - LLM & Multi-Modal)
Tether is hiring a remote AI research engineer to run large-scale pre-training of LLM and multi-modal models on distributed GPU clusters. The role covers architecture and tokenizer design, multi-modal data curation, and debugging training bottlenecks, using PyTorch and Hugging Face.
- Compensation
- Not disclosed
- Location
- Remote job
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Ai engineering
- Posted
Skills
Must have
- Degree in Computer Science or a related field
- Hands-on experience with large-scale LLM or Multi-Modal pre-training runs on distributed servers with thousands of NVIDIA GPUs
- Practical experience with large-scale distributed training frameworks, libraries and tools
- Deep knowledge of state-of-the-art transformer and non-transformer modifications for intelligence, efficiency and scalability
- Strong expertise in PyTorch and Hugging Face libraries, including model development, continual pretraining and deployment
- Excellent English communication skills
Nice to have
- PhD in NLP, Machine Learning, or related field with a solid AI R&D track record and publications in A* conferences
Full 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 TetherTether 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.
But that’s just the beginning:
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.