AI Research Engineer (Pre-training - LLM & Multi-Modal)

Tether; Dublin; On-site; EUR 70,000 - 110,000; Full time

Job summary

Tether in Dublin is seeking a talented AI model team member focused on innovation in model architecture development across various scales. The role involves driving pre-training for large language and multi-modal models using advanced techniques and high-performance computing resources.

The ideal candidate holds a PhD in NLP or a related field, and has extensive experience with NVIDIA GPUs and large-scale training frameworks. Join us in advancing the forefront of AI technology!

Qualifications

• Degree in Computer Science or related field, ideally with a PhD in relevant area.
• Experience in large-scale LLM or Multi-Modal pre-training runs on distributed servers.
• Familiarity with distributed training frameworks and libraries.

Responsibilities

• Conduct foundational pre-training for LLMs and Multi-Modal models.
• Design and prototype innovative architectures for enhanced model intelligence.
• Source and curate large-scale textual and multi-modal datasets.

Skills

Large Language Model expertise

Multi-Modal architecture knowledge

Experience with PyTorch

Hands-on experience with large-scale training

Education

PhD in NLP, Machine Learning or related field

Tools

NVIDIA GPUs

Hugging Face libraries

Job description

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.

Responsibilities

• Large‑Scale Pre‑Training: Conduct foundational pre‑training for LLMs and Multi‑Modal models (integrating text, vision, audio, or other modalities) on large, distributed servers equipped with multi‑nodes and thousands of NVIDIA GPUs.

• Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross‑modal alignment layers to enhance model intelligence and multi‑modal understanding.

• Data Strategy: Source, filter, and curate massive‑scale textual and multi‑modal datasets, establishing robust data pipelines for efficient pre‑training.

• Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for optimal performance and token efficiency.

• Optimization & Debugging: Investigate, debug, and eliminate bottlenecks in model efficiency, computational performance, and multi‑modal alignment stability during long training runs.

• System Scalability: Contribute to the advancement of distributed training systems to ensure seamless scalability and hardware efficiency on target platforms.

Qualifications

• A degree in Computer Science or related field. Ideally a PhD in NLP, Machine Learning, or a related field, with a solid track record in AI R&D and publications in A* conferences.

• Hands‑on experience contributing to large‑scale LLM or Multi‑Modal pre‑training runs on large, distributed servers equipped with thousands of NVIDIA GPUs, ensuring scalability and impactful advancements in model performance.

• 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 aimed at enhancing intelligence, efficiency, and scalability.

• Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pre‑training, and deployment.