GPU Performance Optimization Engineer
2 months ago
We are seeking a highly motivated and experienced GPU Performance Optimization Engineer to join our team at Advanced Micro Devices, Inc. (AMD). As a key member of our datacenter GPU platform performance team, you will be responsible for ensuring that our GPU-accelerated systems operate at peak performance, enabling our customers to solve the world's most challenging problems.
Key Responsibilities- Define Performance Suite and Best Practices: Develop and maintain a comprehensive performance suite and best practices for measuring GPU-accelerated workloads, assessing scalability and efficiency of AI models and algorithms.
- Benchmark and Analyze AI Workloads: Benchmark and analyze AI workloads in single and multi-node configurations, comparing against previous generations and competitors.
- Perform Comprehensive Performance Analysis: Conduct thorough performance analysis and report findings for the entire platform, including GPU, CPU, interconnects, network, software stack, etc.
- Identify and Address Performance Bottlenecks: Identify performance bottlenecks that impact data center GPU-accelerated workloads, collaborate with software teams to improve performance, and implement solutions.
- Stay Up-to-Date with Emerging Technologies: Stay current with emerging technologies and trends, exploring ways to improve the performance of GPU-accelerated workloads at scale.
- Artificial Intelligence and Machine Learning: Solid knowledge of AI and ML concepts and techniques, including deep learning, reinforcement learning, natural language processing, generative AI, and computer vision, with practical experience applying these concepts to solve real-world problems.
- Benchmarking Methodologies: Experience in benchmarking methodologies, performance analysis, workload profiling, performance monitoring, and debugging tools.
- Programming Skills: Advanced Linux OS, container (e.g., Docker) and GitHub skills, with proficiency in programming languages such as Python or C/C++.
- Deep Learning Frameworks: Experience with deep learning frameworks like PyTorch and TensorFlow.
- GPU Architecture: Knowledge and interest in computer and GPU architecture.
- GPU Acceleration: Experience with GPU acceleration with either AMD or Nvidia GPU compute products.
- Education: B.S./M.S./Ph.D. in Computer Science or Engineering or similar field.
- Work Experience: Relevant work experience in GPU performance optimization, AI, and ML.
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