AI Research Engineer (Pre-training - LLM & Multi-Modal)
Tether Operations Limited5 months ago
Remote, WorldwideSenior
Responsibilities
- Conduct foundational pre-training for LLM and multi-modal models on distributed systems with thousands of NVIDIA GPUs.
- Design, prototype, and scale architectures, tokenizers, and cross-modal alignment layers.
- Source, filter, curate, and pipeline large-scale textual and multi-modal datasets.
- Execute experiments, analyze results, and refine training methods for model performance and token efficiency.
- Investigate and resolve model-efficiency, computational-performance, and multi-modal alignment bottlenecks.
- Contribute to distributed training systems and hardware scalability improvements.
Requirements
- Degree in Computer Science or a related field; a PhD in NLP, Machine Learning, or a related field is ideal.
- Hands-on experience contributing to large-scale LLM or multi-modal pre-training runs on distributed servers with thousands of NVIDIA GPUs.
- Practical familiarity 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.
- A solid AI R&D track record with strong publications at A* conferences is preferred.
Benefits
- Remote work with a globally distributed team.
Tech Stack
Categories
AI Research
About Tether Operations Limited
Tether Operations Limited develops and issues reserve-backed digital tokens used by exchanges, wallets, payment processors, and merchants, most notably the USDT stablecoin available across multiple blockchains. Founded in 2014 and privately held, it earns revenue from token issuance, reserve management, and related services, and has expanded into tokenization, peer-to-peer communications (Keet), and Bitcoin mining. USDT is widely used in crypto trading and cross-border transfers, including in emerging markets.