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AMD seeks an Applied Research Scientist to innovate in the field of large language models and multimodal models. This role focuses on advancing state-of-the-art techniques while collaborating within a dynamic research team. Candidates should have a PhD and expertise in ML methodologies to enhance AMD's AI platform.
Join to apply for the Applied Research Scientist role at AMD
Join to apply for the Applied Research Scientist role at AMD
This range is provided by AMD. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more.
$163,360.00/yr - $245,040.00/yr
WHAT YOU DO AT AMD CHANGES EVERYTHING
We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences - the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.
AMD together we advance_
The Role
We are looking for an Applied Research Scientist experienced with training large language models and/or large multimodal models. In this role, you will explore novel LLM/LMM architectures and large-scale training techniques to advance the state-of-the-arts. You will be part of a world-class research team working on pre-training, fine-tuning, and aligning large language and multimodal models, in addition to keeping up to date on the latest progress and trends in LLM/LMM and foundation models.
The Person
Do you like to design and implement novel research ideas, improve the quality of the large language and multimodal models, accelerate the training and inference speed of LLMs/LMMs, and influence future hardware and software direction? If so, this role is for you. The ideal candidate will have expertise and hands-on experience on training LLMs/LMMs, familiar with hyper-parameter tuning techniques, data preprocessing, tokenization methods and latest training approaches for LLMs/LMMs. A successful candidate needs to be knowledgeable with latest transformer architectures.
Key Responsibilities
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