Applied Research Scientist - AI Models & Agents

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE ROLE: The AI Models team is looking for exceptional machine learning scientists and engineers to explore and innovate on training and inference techniques for large language models (LLMs), large multimodal models (LMMs), image/video generation and other foundation models as well as self-evolving agents on top of these. You will be part of a world-class research and development team focussing on efficient and scalable pre-training, instruction tuning, alignment and optimization. As an early member of the team, you can help us shape the direction and strategy to fulfill this important charter. THE PERSON: This role is for you if you are passionate about reading through the latest literature, coming up with novel ideas, and implementing those through high quality code to push the boundaries on scale and performance. The ideal candidate will have both theoretical expertise and hands-on experience with developing and optimizing LLMs, LMMs, and/or diffusion models. KEY RESPONSIBILITIES: Improve upon the state-of-the-art in Generative AI model architectures and their compatibility on AMD accelerators Accelerate the training and inference speed through various optimizations. Build AI agents to automatically write and evaluate efficient kernels and code Drive continuous improvement of infrastructure and development ecosystem Publish your research at top-tier conferences, workshops and/or through technical blogs. Engage with academia and open-source ML communities. PREFERRED EXPERIENCE: Strong development and debugging skills in Python. Experience in deep learning frameworks (like PyTorch or TensorFlow), distributed training tools as well as various agentic AI frameworks Solid understanding of various types of transformers and state space models. Experience in writing kernels using HIP, CUDA, Triton, etc. prior experience in building self-evolving AI agents for various challenging tasks (like code generation, discovery, etc.) knowledge of latest research in the field of AI agents familiar with techniques to do hardware-efficient training and inference of large language models and also familiarity with existing frameworks (e.g. vllm, sglang, etc.) expertise in model architecture (beyond just transformers only models) and latest research in this field having relevant open-source contributions Strong publication record in top-tier conferences, workshops or journals. Solid communication and problem-solving skills. Passionate about learning new stuffs in this domain as well as innovating on top of it ACADEMIC CREDENTIALS: Advanced degree (Master’s or PhD) in machine learning, computer science, artificial intelligence, or a related field is expected. Exceptional Bachelor’s degree candidates with years of relevant research experience may also be considered. #LI-NS2 Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

THE ROLE: The AI Models team is looking for exceptional machine learning scientists and engineers to explore and innovate on training and inference techniques for large language models (LLMs), large multimodal models (LMMs), image/video generation and other foundation models as well as self-evolving agents on top of these. You will be part of a world-class research and development team focussing on efficient and scalable pre-training, instruction tuning, alignment and optimization. As an early member of the team, you can help us shape the direction and strategy to fulfill this important charter. THE PERSON: This role is for you if you are passionate about reading through the latest literature, coming up with novel ideas, and implementing those through high quality code to push the boundaries on scale and performance. The ideal candidate will have both theoretical expertise and hands-on experience with developing and optimizing LLMs, LMMs, and/or diffusion models. KEY RESPONSIBILITIES: Improve upon the state-of-the-art in Generative AI model architectures and their compatibility on AMD accelerators Accelerate the training and inference speed through various optimizations. Build AI agents to automatically write and evaluate efficient kernels and code Drive continuous improvement of infrastructure and development ecosystem Publish your research at top-tier conferences, workshops and/or through technical blogs. Engage with academia and open-source ML communities. PREFERRED EXPERIENCE: Strong development and debugging skills in Python. Experience in deep learning frameworks (like PyTorch or TensorFlow), distributed training tools as well as various agentic AI frameworks Solid understanding of various types of transformers and state space models. Experience in writing kernels using HIP, CUDA, Triton, etc. prior experience in building self-evolving AI agents for various challenging tasks (like code generation, discovery, etc.) knowledge of latest research in the field of AI agents familiar with techniques to do hardware-efficient training and inference of large language models and also familiarity with existing frameworks (e.g. vllm, sglang, etc.) expertise in model architecture (beyond just transformers only models) and latest research in this field having relevant open-source contributions Strong publication record in top-tier conferences, workshops or journals. Solid communication and problem-solving skills. Passionate about learning new stuffs in this domain as well as innovating on top of it ACADEMIC CREDENTIALS: Advanced degree (Master’s or PhD) in machine learning, computer science, artificial intelligence, or a related field is expected. Exceptional Bachelor’s degree candidates with years of relevant research experience may also be considered. #LI-NS2