Principal Solutions Engineer, Infrastructure (SLURM & AI Focus) Principal Solutions Engineer, Infrastructure (SLURM & AI Focus) Get AI-powered advice on this job and more exclusive features. This range is provided by AMD. Your actual pay will be based on your skills and experience — talk with your recruiter to learn more. 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. 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.

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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 & Orchestratio n

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

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.

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