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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Join the AI model team to drive innovation in pre-training for LLMs and multi-modal systems. Lead architecture design, data curation, and experimental methods to advance cross-modal AI performance at scale, collaborating on distributed training and model efficiency.

Location: Rome

Workplace: Remote

Employment Type: Full time

Job Function: Education & Training

Key Responsibilities

•Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers with thousands of NVIDIA GPUs.
•Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers.
•Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines.
•Experimental Research: Independently and collaboratively run experiments, analyze results, and refine training methodologies.
•Optimization & Debugging: Identify and eliminate bottlenecks in model efficiency and multi-modal alignment stability during long runs.

Key Requirements

•A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or 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 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 pretraining, and deployment.

Experience:FintechAIBlockchainLLMMultimodal

Education:Bachelor's

Languages:English

Tech Stack:PyTorchHugging FaceNVIDIA GPUsDistributed trainingTransformers

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website

Loading job details...

Preparing the role view and application actions.

Tether.io

1 month ago

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

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: Rome

Workplace: Remote

Employment Type: Full time

Job Function: Education & Training

Key Responsibilities

•Large-Scale Pre-Training: Conduct foundational pre-training for LLMs and Multi-Modal models on large, distributed servers with thousands of NVIDIA GPUs.
•Architecture & Alignment Innovation: Design, prototype, and scale innovative architectures, tokenizers, and cross-modal alignment layers.
•Data Strategy: Source, filter, and curate massive-scale textual and multi-modal datasets, establishing robust data pipelines.
•Experimental Research: Independently and collaboratively run experiments, analyze results, and refine training methodologies.
•Optimization & Debugging: Identify and eliminate bottlenecks in model efficiency and multi-modal alignment stability during long runs.

Key Requirements

•A degree in Computer Science or related field. Ideally PhD in NLP, Machine Learning, or 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 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 pretraining, and deployment.

Experience:FintechAIBlockchainLLMMultimodal

Education:Bachelor's

Languages:English

Tech Stack:PyTorchHugging FaceNVIDIA GPUsDistributed trainingTransformers

Company Brief

Tether.io

Builds tools and services to help companies hire, onboard, and manage remote or distributed teams across borders, focusing on payroll, compliance, and global employment workflows.

Industry: HR Tech

Website