Principal Solutions Engineer, Infrastructure (SLURM & AI Focus)

WHAT YOU DO AT AMD CHANGES EVERYTHING We care deeply about transforming lives with AMD technology to enrich our industry, our communities, and the world. Our mission is to build great products that accelerate next-generation computing experiences – the building blocks for the data center, artificial intelligence, PCs, gaming and embedded. Underpinning our mission is the AMD culture. We push the limits of innovation to solve the world’s most important challenges. We strive for execution excellence while being direct, humble, collaborative, and inclusive of diverse perspectives. AMD together we advance_ Principal Solutions Engineer, Infrastructure (SLURM & AI Focus) THE ROLE: The AMD Datacenter GPU team is seeking an experienced Solutions Engineer specializing in high-performance computing infrastructure for AI workloads. This role focuses on designing, deploying, and optimizing GPU-accelerated computing environments for AI use-cases using SLURM as the primary workload manager. THE PERSON: The ideal candidate will have deep expertise in Multi-tenant Schedulers for large scale AI Clusters, RDMA networking, collective communications, container orchestration, and storage solutions optimized for AI/ML workloads. KEY RESPONSIBILITIES: AI Infrastructure Design Build and design large GPU-accelerated clusters for AI/ML workloads Develop reference architectures for SLURM-based HPC environments Integrate SLURM with Kubernetes for hybrid workload management Design storage systems to support high-speed AI training pipelines SLURM Optimization & Management Configure and optimize SLURM for efficient AI/ML scheduling and resource use Use advanced SLURM features such as GPU-aware scheduling, MPI integration, container runtime support, and fair-share policies Develop SLURM plugins and customizations for AI workloads Networking & Interconnect Design RDMA network setups (InfiniBand, RoCE) for fast data transfer Optimize collective communications for distributed training (e.g., All Reduce) Configure GPU Direct RDMA and topology-aware job scheduling Storage Solutions Architect parallel file systems like Lustre, GPFS, BeeGFS for AI data needs Implement high-performance scratch storage and tiered data management Optimize I/O patterns and manage data lifecycle for training datasets Container Orchestration & Integration Collaborate on Kubernetes operators for SLURM integration Develop strategies for seamless containerized AI workload management Build CI/CD pipelines and enable hybrid cloud deployments Collaboration & Support Work with research teams and customers to meet AI computing needs Provide technical guidance and training Create documentation and best practices Partner with vendors on hardware and software selection PREFERRED EXPERIENCE: Technical Skills Extensive SLURM experience in production HPC environments Expert knowledge of RDMA technologies and collective communications Hands-on GPU computing and Linux system administration skills Experience with parallel file systems and scripting (Python, Bash, Go) Container & Orchestration Production Kubernetes experience in HPC settings Familiarity with Kubernetes SLURM plugin and container runtimes (Singularity, Docker) Experience with Helm and Kubernetes operators AI/ML Infrastructure Understanding AI frameworks (PyTorch, TensorFlow, JAX) and distributed training Knowledge of AI workload optimization and MLOps practices EDUCATION: Bachelor’s degree in Computer Science, Engineering, or related field Advanced degree preferred #LI-EV1 #LI-HYBRID 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. 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. Principal Solutions Engineer, Infrastructure (SLURM & AI Focus) THE ROLE: The AMD Datacenter GPU team is seeking an experienced Solutions Engineer specializing in high-performance computing infrastructure for AI workloads. This role focuses on designing, deploying, and optimizing GPU-accelerated computing environments for AI use-cases using SLURM as the primary workload manager. THE PERSON: The ideal candidate will have deep expertise in Multi-tenant Schedulers for large scale AI Clusters, RDMA networking, collective communications, container orchestration, and storage solutions optimized for AI/ML workloads. KEY RESPONSIBILITIES: AI Infrastructure Design Build and design large GPU-accelerated clusters for AI/ML workloads Develop reference architectures for SLURM-based HPC environments Integrate SLURM with Kubernetes for hybrid workload management Design storage systems to support high-speed AI training pipelines SLURM Optimization & Management Configure and optimize SLURM for efficient AI/ML scheduling and resource use Use advanced SLURM features such as GPU-aware scheduling, MPI integration, container runtime support, and fair-share policies Develop SLURM plugins and customizations for AI workloads Networking & Interconnect Design RDMA network setups (InfiniBand, RoCE) for fast data transfer Optimize collective communications for distributed training (e.g., All Reduce) Configure GPU Direct RDMA and topology-aware job scheduling Storage Solutions Architect parallel file systems like Lustre, GPFS, BeeGFS for AI data needs Implement high-performance scratch storage and tiered data management Optimize I/O patterns and manage data lifecycle for training datasets Container Orchestration & Integration Collaborate on Kubernetes operators for SLURM integration Develop strategies for seamless containerized AI workload management Build CI/CD pipelines and enable hybrid cloud deployments Collaboration & Support Work with research teams and customers to meet AI computing needs Provide technical guidance and training Create documentation and best practices Partner with vendors on hardware and software selection PREFERRED EXPERIENCE: Technical Skills Extensive SLURM experience in production HPC environments Expert knowledge of RDMA technologies and collective communications Hands-on GPU computing and Linux system administration skills Experience with parallel file systems and scripting (Python, Bash, Go) Container & Orchestration Production Kubernetes experience in HPC settings Familiarity with Kubernetes SLURM plugin and container runtimes (Singularity, Docker) Experience with Helm and Kubernetes operators AI/ML Infrastructure Understanding AI frameworks (PyTorch, TensorFlow, JAX) and distributed training Knowledge of AI workload optimization and MLOps practices EDUCATION: Bachelor’s degree in Computer Science, Engineering, or related field Advanced degree preferred #LI-EV1 #LI-HYBRID