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

Tether.io; Indiana (PA); On-site; USD 120,000 - 160,000; Full time

Job summary

Tether.io is seeking an AI model expert to drive innovation in architecture development for cutting-edge models. You will work on large-scale pre-training for LLMs and Multi-Modal systems, utilizing a hands-on, research-driven approach to enhance performance.

The ideal candidate will hold a PhD in a relevant field and possess deep expertise in Large Language Models and Multi-Modal architectures, as well as experience with distributed training on servers equipped with multiple NVIDIA GPUs.

Qualifications

• PhD in NLP, Machine Learning, or related field with a strong AI R&D track record.
• Hands-on experience with large-scale LLM or Multi-Modal pre-training on distributed servers.
• Deep knowledge of transformer and non-transformer methods.

Responsibilities

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

Skills

Large Language Models (LLM)

Multi-Modal systems

PyTorch

Hugging Face libraries

Data strategy

Education

PhD in NLP, Machine Learning or related field

Degree in Computer Science or related field

Tools

Distributed training frameworks

NVIDIA GPUs

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 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 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 & 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 PhD in NLP, Machine Learning, or a related field, complemented by a solid track record in AI R&D (with good 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.