AI Performance Optimization Expert

• Master’s or Ph.D. degree in Computer Architecture, Compiler Design, High Performance Computing, or a related field.
• Deep understanding of GPU or NPU architecture, including execution units, memory hierarchy, interconnects, and thread scheduling, as well as performance bottleneck analysis methodologies.
• Familiarity with mainstream deep learning frameworks such as PyTorch, TensorFlow, or JAX.
• Hands-on experience in deep learning operator/kernel development and performance tuning, with the ability to implement and optimize complex operators.
• Proficiency with performance analysis and profiling tools (e.g., Nsight Compute, nvprof, torch.profiler), and ability to conduct quantitative analysis and performance modeling.
• Strong system design and software engineering skills, with the ability to balance performance, maintainability, and generality in complex systems.

• Lead performance optimization of AI models on Ascend NPUs, including performance analysis, bottleneck identification, and optimization implementation for both training and inference workloads.
• Analyze performance bottlenecks of multimodal models and large language models (LLMs) on the Ascend platform, covering operators, kernels, memory access patterns, and scheduling; design and implement optimization strategies.
• Develop and optimize critical operators/kernels, continuously improving execution efficiency, memory access patterns, parallelization strategies, and hardware resource utilization.
• Research and apply advanced techniques such as auto-tuning, operator fusion, graph optimization, and scheduling optimization in real-world production scenarios.
• Build and lead an NPU performance optimization team; communicate findings to cross-functional teams and leadership, and contribute to the evolution of next-generation Ascend NPU architecture.

Full time office work in Wola, Warsaw.