Remote AI Research Engineer: LLM & Multimodal Pre-Training

tether; Abu Dhabi; Remote; AED 441,000 - 661,000; Full time

Job summary

Tether is hiring for an AI model engineer to drive architecture development for large-scale LLMs and multi-modal systems. You will contribute across pre-training, model alignment, and data strategies, leveraging distributed systems and cutting-edge frameworks.

The role requires deep expertise in LLMs, cross-modal architectures, and robust research-driven approaches, with opportunities to shape next-gen fintech AI capabilities and scalable deployment.

Qualifications

• A degree in Computer Science or related field; PhD in NLP/ML preferred.
• Strong publications in AI R&D and top conferences (A* venues).
• Hands-on experience with large-scale LLM or multi-modal pre-training.
• Experience with distributed training on thousands of GPUs.
• Familiarity with large-scale distributed frameworks and tools.

Responsibilities

• Large-Scale Pre-Training: conduct foundational pre-training for LLMs and multi-modal models on distributed servers with many GPUs.
• Architecture & Alignment Innovation: design and scale novel architectures and cross-modal alignment layers.
• Data Strategy: curate massive-scale textual and multi-modal datasets and pipelines.
• Experimental Research: run experiments, analyze results, refine training methods.
• Optimization & Debugging: identify and fix bottlenecks in training efficiency and alignment stability.
• System Scalability: advance distributed training systems for hardware-efficient scaling.

Skills

LLM pre-training

Distributed training

Transformer models

PyTorch

Hugging Face

Education

Bachelors in Computer Science or related field

PhD in NLP/ML (preferred)

Tools

NVIDIA GPUs

Distributed training frameworks

Job description

Tether is hiring for an AI model engineer to drive architecture development for large-scale LLMs and multi-modal systems. You will contribute across pre-training, model alignment, and data strategies, leveraging distributed systems and cutting-edge frameworks.

The role requires deep expertise in LLMs, cross-modal architectures, and robust research-driven approaches, with opportunities to shape next-gen fintech AI capabilities and scalable deployment.