AI Research Engineer – Pre-training, LLM, Multi-Modal

Tether.to

AI Research Engineer advancing LLM and multimodal pre-training for Tether’s blockchain-powered digital finance and AI products. Designing architectures, curating datasets, and scaling distributed GPU training.

Posted 8/10/2026full-timeRemote • 🇬🇧 United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in large-scale LLM and multi-modal model pre-training, with a strong focus on distributed training systems, data pipeline establishment, and model efficiency optimization. Proficient in utilizing advanced frameworks and libraries such as PyTorch and Hugging Face for innovative architecture design and implementation.

Highest-signal resume keywords

Large-Scale LLM Pre-TrainingDistributed Training FrameworksPyTorch ExpertiseHugging Face LibrariesData Pipeline Development

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningNatural Language ProcessingModel DevelopmentToken Efficiency OptimizationCross-Modal AlignmentData CurationExperiment AnalysisComputational Performance DebuggingTransformer ModificationsMulti-Modal Models

Soft Skills

Excellent English Communication

Tools & Technologies

NVIDIA GPUsDistributed ServersData PipelinesTokenizersArchitectures

Certifications & Qualifications

PhD in NLP or Machine Learning

Industry Keywords

AI R&DA* ConferencesScalabilityHardware EfficiencyContinual Pretraining

Tech Stack

Tools & technologies

PyTorch

About the role

Key responsibilities & impact

Conduct foundational pre-training for LLMs and multi-modal models integrating text, vision, audio, or other modalities on large distributed servers with multiple nodes and thousands of NVIDIA GPUs
Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers
Source, filter, and curate large-scale textual and multi-modal datasets and establish robust data pipelines
Execute experiments independently and collaboratively, analyze results, and refine training methodologies for performance and token efficiency
Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi-modal alignment stability during long training runs
Advance distributed training systems for scalability and hardware efficiency

Requirements

What you’ll need

A degree in Computer Science or related field
Ideally PhD in NLP, Machine Learning, or a related field
Solid track record in AI R&D
Good publications in A* conferences
Hands-on experience contributing to large-scale LLM or multi-modal pre-training runs on distributed servers with thousands of NVIDIA GPUs
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 for intelligence, efficiency, and scalability
Strong expertise in PyTorch and Hugging Face libraries
Practical experience in model development, continual pretraining, and deployment
Excellent English communication skills

Benefits

Comp & perks

Remote work from every corner of the world
Opportunity to collaborate with a global talent powerhouse
Opportunity to contribute to innovative fintech, blockchain, AI, and digital finance products