Datacenter GPU Platform Performance Engineer

Key Skills

AI technology, C Programming, C++, Computer Vision (CV), Deep Learning, Docker, Generative AI, GPUs, Linux Operating system, Machine learning techniques, Natural Language Processing (NLP), Python Programming, PyTorch, Reinforcement Learning, TensorFlow

Job Description

WHAT YOU DO AT AMD CHANGES EVERYTHING

We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives.

AMD together we advance_

Datacenter GPU Platform Performance Engineer

THE ROLE:

As part of our team, you'll be responsible for ensuring that AMD Instinct GPU-accelerated systems are operating at peak performance before being deployed to solve the world's most challenging problems. We're looking for a highly motivated candidate with expertise in GPU performance and familiarity with performance monitoring and tuning tools. The ideal candidate should also possess data science and communication skills to effectively convey their findings to engineering and business teams.

We value curiosity and innovation, and we're committed to providing a challenging and supportive environment where you can learn and grow. As you collaborate with your peers, you'll have the opportunity to make a real impact and contribute to our organization's success. We're looking for someone who's passionate about improving their skills and constantly seeking new ways to drive performance and efficiency. If you're passionate about performance engineering and want to make a real impact with customers deploying the latest AI breakthroughs, we encourage you to apply. 

KEY RESPONSIBILITIES:

• Define performance suite and best practices for measuring GPU-accelerated workloads to assess scalability and efficiency of AI models and algorithms
• Benchmark and analyze AI workloads in single and multi-node configurations comparing against previous generations and our competitors
• Perform comprehensive performance analysis and report findings for the entire platform including GPU, CPU, interconnects, network, software stack, etc.
• Identify performance bottlenecks that impact data center GPU-accelerated workloads, tune and collaborate with other software teams to improve performance
• Stay up to date with emerging technologies and trends and explore ways to improve the performance of GPU-accelerated workloads at scale

PREFERRED EXPERIENCE:

• Solid knowledge of Artificial Intelligence (AI) and Machine Learning (ML) concepts and techniques, including deep learning, reinforcement learning, natural language processing, generative AI, and computer vision, as well as practical experience applying these concepts to solve real-world problems through research or work experience
• Experience in benchmarking methodologies, performance analysis, workload profiling, performance monitoring and debugging tools
• Advanced Linux OS, container (e.g. Docker) and GitHub skills
• Programming skills in a variety of relevant languages such as Python or C/C++
• Expertise with deep learning frameworks like PyTorch and TensorFlow
• Knowledge and interest in computer and GPU architecture
• In-depth knowledge of GPU acceleration with either AMD or Nvidia GPU compute products
• Inquiring mind, excellent problem-solving skills, and automation mindset

ACADEMIC CREDENTIALS:

• BS./M.S./PhD in Computer Science or Engineering or similar field