Senior AI Cluster Hardware Engineer

The Role We are seeking a highly motivated and skilled GPU Cluster Network Performance Attainment Engineer to join our dynamic team. In this role, you will be at the forefront of optimizing and achieving peak performance for GPU clusters, focusing on the RDMA networks used in AI Clusters and understanding data flows between GPU, NIC and cluster network. The ideal candidate will have a strong background in GPU architectures, parallel computing, and hands‑on experience in system‑level performance tuning and debug methodologies. Key Responsibilities Scalability Testing: Evaluate the scalability of GPU clusters by conducting thorough testing under various workloads, ensuring optimal performance across different cluster sizes, configurations, and networking technologies (RoCE & IB). Performance Profiling: Utilize profiling tools and methodologies to analyze and identify performance bottlenecks, providing actionable insights for improvement. Performance Tuning: Implement optimization strategies, including protocol enhancements, load balancing techniques, and parallel processing optimizations. Documentation: Create detailed documentation of performance analysis, tuning efforts, and outcomes, providing clear and concise reports for internal teams and stakeholders. Collaboration: Work closely with cross‑functional teams, including hardware engineers, software developers, and system architects, to integrate performance improvements into the GPU cluster architecture. NIC & Performance Optimization: Collaborate with hardware and software teams to enhance the overall performance of GPU clusters, focusing on RDMA throughput, latency, and collective communications. Benchmarking and Analysis: Develop and execute comprehensive benchmarking strategies to assess baseline performance, analyze bottlenecks, and identify areas for improvement within GPU cluster environments. Continuous Learning: Stay current with the latest developments in GPU architectures, parallel processing, and emerging technologies to drive continuous improvement in GPU cluster performance. Preferred Experience Proven experience in optimizing the performance of GPU clusters. Understanding of RDMA network drivers. Strong understanding of GPU architectures, parallel computing concepts, and network protocols. Proficiency in scripting languages (e.g., Python, Bash) for automation and performance analysis. Experience with system‑level performance analysis tools and methodologies for GPU clusters. Analytical mindset with excellent problem‑solving and debug skills. Familiarity with cluster management tools and systems. Excellent communication and collaboration skills for effective teamwork. RDMA network configuration, troubleshooting and performance tuning. Linux kernel networking expertise. Machine learning and/or HPC system design. Academic Credentials Bachelor’s or Master’s degree in electrical or computer engineering preferred. Location: Austin, TX This role is not eligible for visa sponsorship. 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. #J-18808-Ljbffr AMD