Staff Applied Machine Learning Software Engineer Generative AI

AMD - San Jose, CA

Full-time - Senior

San Jose, CA

Computer and Electronic Product Manufacturing

About the position

The Staff Applied Machine Learning Software Engineer - Generative AI at AMD is responsible for developing and optimizing software solutions for Generative AI applications. This role involves working on state-of-the-art research, model optimization, and compression algorithms to enhance AMD's AI inference capabilities across various products. The engineer will collaborate with multiple teams to influence the direction of AI/ML platforms and ensure high-quality software development.

Responsibilities

• Accelerate inference of Generative AI on AMD's products.
• Develop tools and techniques for model analysis, profiling, performance projections, and analyzing architecture bottlenecks.
• Architect and prototype custom kernels on GPUs and CPUs (HIP, CUDA, OpenCL, Triton, etc.).
• Optimize deep learning inference pipeline including graph compilation using AMD AI compilers.
• Reproduce and improve upon state-of-the-art quantization, pruning, optimization algorithms in Pytorch and Python.
• Develop high quality software to enable next-gen solutions.
• Collaborate with AI/ML frameworks and infrastructure teams to enable new algorithms in the platforms.
• Collaborate with AMD Research and Architecture teams to improve future products.
• Influence the direction of AI/ML platforms for inference and training.

Requirements

• Deep understanding of foundational AI model architectures.
• Proficiency in software development and AI/ML frameworks such as Pytorch.
• Experience with quantization and sparsity algorithms.
• Demonstrated ability to efficiently map AI models onto GPUs and/or other hardware accelerators.
• Strong development and debugging skills in Python.
• Experience in C++ programming for GPUs and/or custom accelerators.

Nice-to-haves

• Solid understanding of CNN, Transformer model architectures, LLMs, Stable Diffusion models.
• Familiarity with torch.fx and/or other AI compilers, execution runtime.
• Knowledge of LLMs/LMMs finetuning methods like RLHF.
• Knowledge of parameter efficient techniques like LoRA QLoRA.
• Contributions to open ML research or developer community.

Benefits

• Employee stock purchase plan
• Competitive benefits package