Work on generative AI for image generation and help turn diffusion-based and multimodal research into practical performance on AMD GPU architectures. This onsite role in San Jose, California supports training and inference pipelines, model evaluation, and optimization efforts across research, software, and hardware teams. AMD benefits at a glance are available.
Responsibilities
- Develop, train, fine-tune, and optimize generative AI models for image generation and visual content creation.
- Design and implement diffusion models, multimodal architectures, and advanced machine learning algorithms.
- Build scalable training and inference pipelines using high-performance GPU computing.
- Evaluate model quality, performance, and efficiency using state-of-the-art techniques and benchmarks.
- Collaborate with research, software, and hardware teams to integrate AI innovations into AMD technologies.
- Optimize AI workloads for AMD GPU architectures and contribute to performance analysis and improvements.
- Stay current with emerging advancements in generative AI, image synthesis, diffusion models, and large-scale machine learning.
Requirements
- Strong software development experience in Python and C++.
- Deep understanding of machine learning, deep learning, and neural network architectures.
- Experience developing and training diffusion models, image generation models, GANs, transformers, or multimodal AI systems.
- Hands-on experience with PyTorch, TensorFlow, JAX, or similar machine learning frameworks.
- Experience fine-tuning and deploying large-scale AI models.
- Familiarity with distributed training, model optimization, and GPU acceleration techniques.
- Experience building AI training and inference pipelines in production environments.
- Knowledge of computer vision, image synthesis, and multimodal learning techniques.
- Experience with MLOps, CI/CD workflows, Git, and modern software engineering practices.
- Experience leveraging AI coding assistants, agentic AI systems, or automated development workflows.
- Strong communication, collaboration, and problem-solving skills.
Technologies
Python, C++, diffusion models, GANs, transformers, multimodal AI systems, PyTorch, TensorFlow, JAX, MLOps, CI/CD, Git, AI coding assistants, agentic AI systems.
Education
Master’s degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, Computer Engineering, or a related field.
