layiq

/la·yiq/adjective

worthy; deserving; fitting; suitable.

A role, opportunity, or path that merits attention, time, and pursuit.

Principal Engineer, Efficient GenAI at AMD | LAYIQ

Propose and apply innovative techniques to support both training and inference, including innovative transformer architectures, parallelism strategies for training on large clusters, inference optimization techniques such as speculative decoding, and optimal KV-caching strategies.
Implement novel, efficient architectures for Generative AI models for training and inference and showcase the benefits on AMD platforms.
Work with open-source frameworks and communities (e.g., PyTorch, JAX, vLLM, SGLang) to integrate AMD-optimized models and libraries and publish training recipes.
Collaborate with software and hardware teams to co-optimize end-to-end performance on current and future AMD solutions.
Increase adoption of agentic workflows for optimizing and deploying Generative AI applications at scale on AMD platforms.
Publish and promote your work at external venues, including major conferences.
Collaborate with researchers within AMD and across industry and academia to promote innovation on AMD platforms.
PREFERRED EXPERIENCE: Strong technical expertise in Generative AI model training and inference, with familiarity working with deep learning frameworks such as PyTorch, JAX, vLLM, SGLang, and MuJoCo.
Strong technical expertise in algorithmic innovation for efficient Generative AI applications across both training and inference.
Expertise and publications in one or more of the following preferred areas: efficient model architectures, optimized training, innovative parallelism strategies, or low-precision training.
Additional plus if publications have been presented at conferences such as NeurIPS, CVPR, ECCV, ICCV, ICML, or ICLR.
Experience productizing Generative AI models and training foundation models at scale.
Excellent written, verbal, and presentation skills, with the ability to coordinate effectively both internally and externally.
Several years of experience in AI, deep learning, and related software development.
ACADEMIC CREDENTIALS: PhD or master’s degree in computer science, Electrical Engineering, Mathematics, or a related field.
LOCATION: San Jose, CA (Hybrid)
Alternative locations in Seattle, WA, or Austin, TX may be considered.

Responsibilities

THE ROLE:

The AI Models and Applications team at AMD is looking for a specialized Principal Engineer who is passionate about enabling innovative and efficient Generative AI training and inference at scale. You will be part of a core team of incredibly talented specialists and work on scaling training and inference for the latest Generative AI models.

THE PERSON:

You have a deep technical understanding and hands-on experience with the latest Generative AI applications in at least one of the following areas: large language models (LLMs), 3D World and Action Models, or image/video generation models. You have experience training models at scale and are passionate about developing efficient approaches to enable distributed training and inference on AMD devices.

Why Join Us?

Exciting Opportunities:

As a senior member of the team, you will be at the forefront of innovation, working with the latest Generative AI models and algorithms. You will have the opportunity to shape the future of AI model training and inference optimization across a variety of applications.

Talented Team:

Join a team of highly skilled industry specialists who are passionate about pushing the boundaries of AI. Collaborate with like-minded professionals and learn from the best in the field.

Cutting-Edge Technology:

Work with state-of-the-art Generative AI algorithms and software, enabling you to stay ahead of the curve and drive advancements in AI model training at scale and deployment.

Impactful Work:

Your contributions will directly influence how cutting-edge Generative AI models across the industry are efficiently trained at scale, as well as how inference solutions are deployed to serve millions of customers, making a significant difference across various industries and applications.

KEY RESPONSIBILITIES:

Propose and apply innovative techniques to support both training and inference, including innovative transformer architectures, parallelism strategies for training on large clusters, inference optimization techniques such as speculative decoding, and optimal KV-caching strategies.
Implement novel, efficient architectures for Generative AI models for training and inference and showcase the benefits on AMD platforms.
Work with open-source frameworks and communities (e.g., PyTorch, JAX, vLLM, SGLang) to integrate AMD-optimized models and libraries and publish training recipes.
Collaborate with software and hardware teams to co-optimize end-to-end performance on current and future AMD solutions.
Increase adoption of agentic workflows for optimizing and deploying Generative AI applications at scale on AMD platforms.
Publish and promote your work at external venues, including major conferences.
Collaborate with researchers within AMD and across industry and academia to promote innovation on AMD platforms.
PREFERRED EXPERIENCE: Strong technical expertise in Generative AI model training and inference, with familiarity working with deep learning frameworks such as PyTorch, JAX, vLLM, SGLang, and MuJoCo.
Strong technical expertise in algorithmic innovation for efficient Generative AI applications across both training and inference.
Expertise and publications in one or more of the following preferred areas: efficient model architectures, optimized training, innovative parallelism strategies, or low-precision training.
Additional plus if publications have been presented at conferences such as NeurIPS, CVPR, ECCV, ICCV, ICML, or ICLR.
Experience productizing Generative AI models and training foundation models at scale.
Excellent written, verbal, and presentation skills, with the ability to coordinate effectively both internally and externally.
Several years of experience in AI, deep learning, and related software development.
ACADEMIC CREDENTIALS: PhD or master’s degree in computer science, Electrical Engineering, Mathematics, or a related field.
LOCATION: San Jose, CA (Hybrid)
Alternative locations in Seattle, WA, or Austin, TX may be considered.

Principal Engineer, Efficient GenAI

Employer job description

THE ROLE:

THE PERSON:

Exciting Opportunities:

Talented Team:

Cutting-Edge Technology:

Impactful Work:

KEY RESPONSIBILITIES:

Benefits offered are described:

Responsibilities

THE ROLE:

THE PERSON:

Exciting Opportunities:

Talented Team:

Cutting-Edge Technology:

Impactful Work:

KEY RESPONSIBILITIES:

Qualifications

Benefits offered are described:

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