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

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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 architecture development for LLM and multi-modal systems. Lead large-scale pre-training on distributed GPU clusters, design novel architectures and alignment methods, curate multi-modal data, and optimize training efficiency and scalability across hardware.

Location: Zurich

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 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 for pre-training.
•Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for performance and efficiency.
•Optimization & Debugging: Investigate and eliminate bottlenecks in model efficiency, performance, and multi-modal alignment stability during long runs.

Key Requirements

•A degree in Computer Science or related field; ideally a PhD in NLP, Machine Learning, or related field with a strong AI R&D record.
•Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training on thousands of NVIDIA GPUs, with scalable training in distributed environments.
•Familiarity with large-scale distributed training frameworks, libraries and tools.
•Deep knowledge of state-of-the-art transformer and non-transformer modifications to improve intelligence, efficiency and scalability.
•Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment.

Education:Bachelor's

Skills:English

Languages:English

Tech Stack:PyTorchHugging FaceNVIDIA GPUsDistributed trainingPythonDeep learning

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

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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: Zurich

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 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 for pre-training.
•Experimental Research: Independently and collaboratively execute experiments, analyze results, and refine training methodologies for performance and efficiency.
•Optimization & Debugging: Investigate and eliminate bottlenecks in model efficiency, performance, and multi-modal alignment stability during long runs.

Key Requirements

•A degree in Computer Science or related field; ideally a PhD in NLP, Machine Learning, or related field with a strong AI R&D record.
•Hands-on experience contributing to large-scale LLM or Multi-Modal pre-training on thousands of NVIDIA GPUs, with scalable training in distributed environments.
•Familiarity with large-scale distributed training frameworks, libraries and tools.
•Deep knowledge of state-of-the-art transformer and non-transformer modifications to improve intelligence, efficiency and scalability.
•Strong expertise in PyTorch and Hugging Face libraries with practical experience in model development, continual pretraining, and deployment.

Education:Bachelor's

Skills:English

Languages:English

Tech Stack:PyTorchHugging FaceNVIDIA GPUsDistributed trainingPythonDeep learning

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