Full Job Description
Responsibilities
THE ROLE:
Join a team developing AI software technologies that enable state-of-the-art models and frameworks to run efficiently on GPU-accelerated platforms. This role spans the end-to-end AI software stack from model bring-up and framework enablement to performance optimization and GPU software acceleration and offers the opportunity to contribute to open-source AI ecosystems supporting generative AI, agentic AI, large language models, image generation, and other emerging workloads across client, consumer, and enterprise platforms.
THE PERSON:
You are a strong software engineer with solid knowledge of AI software stacks and an interest in working across models, frameworks, and GPU acceleration. You are comfortable learning quickly in a fast-moving technical domain, solving complex problems, and collaborating with distributed teams across software, compiler, platform, and product areas. You communicate clearly, take ownership of technical challenges, and are motivated to build practical AI solutions that scale to real users.
KEY RESPONSIBILITIES:
Develop AI software solutions that enable efficient execution of models and frameworks on GPU-accelerated platforms.
Enable and optimize AI frameworks, inference stacks, and agentic AI software.
Bring up, analyze, and improve the performance of state-of-the-art AI models.
Optimize AI workloads across the software stack, from model architecture to GPU acceleration.
Collaborate with cross-functional teams to deliver end-to-end AI software features and capabilities.
Contribute to open-source AI software projects and support customer enablement efforts.
PREFERRED EXPERIENCE:
Experience developing software in C/C++ and/or Python on Linux and Windows platforms.
Strong understanding of AI/ML concepts, model architectures, and inference workflows.
Experience with AI frameworks and technologies such as PyTorch, vLLM, llama.cpp, or similar ecosystems.
Familiarity with GPU acceleration, performance optimization, compiler technologies, or low-level programming.
Experience analyzing and improving the performance of AI workloads and operators.
Open-source software development experience and interest in emerging AI technologies are a plus.
ACADEMIC CREDENTIALS:
Bachelors, Master, or PhD in Computer Science, Electrical Engineering or relevant fields preferred
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Qualifications
Benefits offered are described: AMD benefits at a glance.
