AI Product Performance Engineer

WHAT YOU DO AT AMD CHANGES EVERYTHING At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond. Together, we advance your career. THE PERSON: Success in this role will require deep knowledge of Data Center AI workloads such as LLM, Generative AI, Recommendation, NLP, Video Analytics, and/or transformer … AI cross cloud, client, edge… the candidate needs to have hands-on experiences with various AI models, end-to-end pipeline, industry framework / SDKs and solutions. KEY RESPONSIBILITIES: 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. PREFERRED EXPERIENCE: GPU Architecture Mastery: In-depth understanding of modern GPU underlying architectures, including streaming multiprocessors (SMs/CUs), memory hierarchy (registers, shared memory, L1/L2 cache, HBM), and warp/wavefront execution models. 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, interpreting low-level metrics to drive architectural-aware optimizations. 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 for agile kernel development and auto-tuning. 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. ACADEMIC CREDENTIALS: BS required. MS preferred with several years of relevant industry experience #LI-FL1 Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

THE PERSON: Success in this role will require deep knowledge of Data Center AI workloads such as LLM, Generative AI, Recommendation, NLP, Video Analytics, and/or transformer … AI cross cloud, client, edge… the candidate needs to have hands-on experiences with various AI models, end-to-end pipeline, industry framework / SDKs and solutions. KEY RESPONSIBILITIES: 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. PREFERRED EXPERIENCE: GPU Architecture Mastery: In-depth understanding of modern GPU underlying architectures, including streaming multiprocessors (SMs/CUs), memory hierarchy (registers, shared memory, L1/L2 cache, HBM), and warp/wavefront execution models. 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, interpreting low-level metrics to drive architectural-aware optimizations. 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 for agile kernel development and auto-tuning. 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. ACADEMIC CREDENTIALS: BS required. MS preferred with several years of relevant industry experience #LI-FL1

Benefits offered are described: AMD benefits at a glance. AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law. We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process. AMD may use Artificial Intelligence to help screen, assess or select applicants for this position. AMD’s “Responsible AI Policy” is available here. This posting is for an existing vacancy.

Tags: No, CNY ¥570,010.00/Yr., CNY ¥814,300.00/Yr., Global Careers (do not use for US or Canada)