AI Research Engineer (Pre-training - LLM & Multi-Modal)
Tether Operations Limited5 months ago
Remote, WorldwideSenior
Responsibilities
- Conduct foundational large-scale pre-training for LLMs and multi-modal models integrating text, vision, audio, and other modalities.
- Design, prototype, and scale novel model architectures, tokenizers, and cross-modal alignment layers.
- Source, filter, and curate large textual and multi-modal datasets while building efficient data pipelines.
- Execute experiments, analyze results, and refine training methodologies for performance and token efficiency.
- Debug and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability.
- Advance distributed training systems for scalability and hardware efficiency on target platforms.
Requirements
- Bachelor's degree in Computer Science or a related field.
- PhD in NLP, Machine Learning, or a related field is preferred.
- Strong track record in AI research and development, including good publications in A* conferences, is preferred.
- Hands-on experience contributing to 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 model intelligence, efficiency, and scalability.
- Strong expertise in PyTorch and Hugging Face libraries, with practical experience in model development, continual pre-training, and deployment.
- Excellent English communication skills.
Benefits
- Remote work from locations around the world.
- Applications must be submitted through Tether's official careers channels; recruitment communication uses official company emails and platforms.
Tech Stack
Categories
AI ResearchML Engineering
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.