AI Product Performance Engineer

• High-Performance Kernel Development: Design, implement, and optimize high-performance GPU kernels for AI/ML workloads to maximize hardware utilization
• Performance Optimization: Analyze and optimize kernel execution for latency and throughput, addressing bottlenecks in memory bandwidth, instruction latency, and thread divergence
• Workload Analysis: Evaluate the end-to-end performance impact of individual kernels on full-stack AI models, ensuring that micro-optimizations translate to application-level speedups
• Profiling & Tuning: Utilize advanced GPU profiling tools (e.g., ROCm Profiler, Pytorch Profiler) to identify performance cliffs, stall pipelines, and memory hierarchy inefficiencies
• Architecture Adaptation: Tailor implementation strategies to leverage specific features of modern GPU architectures (e.g., Matrix Cores, HBM characteristics)
• Framework Integration: Collaborate with software stack teams to expose optimized kernels within high-level frameworks and inference engines

• deep knowledge of Data Center AI workloads such as LLM, Generative AI, Recommendation, NLP, Video Analytics, and/or transformer
• hands-on experiences with various AI models, end-to-end pipeline, industry framework / SDKs and solutions
• GPU Architecture Mastery
• Kernel Programming Expertise: Strong proficiency in C++ and parallel computing, with extensive hands-on experience in NVIDIA CUDA or AMD HIP kernel programming
• Performance Engineering: Demonstrated ability to debug and profile complex GPU workloads
• Systems Knowledge: Familiarity with asynchronous execution, stream management, and host-device memory transfers
• Python DSLs & Triton: Experience implementing kernels using OpenAI Triton or other Python-based DSLs
• Inference Engine Experience: Hands-on experience integrating custom kernels into large-scale inference frameworks such as vLLM, SGLang, or TensorRT-LLM
• Deep Learning Frameworks: Familiarity with writing custom extensions or operators for PyTorch (C++/CUDA extensions)
• Hardware Agnosticism: Experience porting kernels between NVIDIA and AMD architectures or working with cross-platform HPC libraries
• BS required
• MS preferred with several years of relevant industry experience

AMD benefits at a glance