HPC Systems Engineer
Position Objectives
The HPC Systems Engineer will design, deploy, and optimize high-performance computing (HPC) clusters and GPU-enabled servers to support compute-intensive workloads in a cutting-edge environment. This role focuses on delivering robust, secure, and high-performing systems to empower researchers, data scientists, and engineers, ensuring seamless operation of HPC infrastructure while aligning with organizational goals and compliance standards.
Job Description and Responsibilities
- Design, deploy, and maintain HPC clusters and GPU-enabled servers for compute-intensive workloads, ensuring high availability and performance.
- Administer and troubleshoot Red Hat Enterprise Linux systems in production environments to ensure stability and uptime.
- Develop and maintain automation scripts (e.g., Ansible, Bash, Python) for system provisioning, configuration management, and patching.
- Manage job scheduling systems (e.g., Slurm, PBS, LSF) and parallel file systems (e.g., Lustre, GPFS, BeeGFS) for efficient workload distribution.
- Configure and optimize GPU workloads using NVIDIA CUDA or ROCm in HPC/AI environments.
- Collaborate with researchers, data scientists, and engineering teams to fine-tune workloads and enhance system performance.
- Ensure system security, stability, and compliance with organizational policies through proactive measures.
- Monitor system health, analyze performance metrics, and conduct capacity planning to meet future demands.
- Provide technical support, comprehensive documentation, and user training for HPC/GPU systems to enable seamless user experiences.
Qualifications & Skills
- Bachelor’s degree in Computer Science, Information Technology, or a related field.
- Minimum of 5-7 years of experience in HPC systems administration or related roles, with expertise in Red Hat Enterprise Linux.
- Proficiency in automation scripting (Ansible, Bash, Python) and managing job schedulers (Slurm, PBS, LSF) and parallel file systems (Lustre, GPFS, BeeGFS).
- Hands-on experience with GPU workload configuration (NVIDIA CUDA, ROCm) in HPC/AI environments.
- Red Hat Certified Engineer (RHCE) or higher certification is preferred.
- Experience in academic, research, or enterprise-scale HPC environments, with exposure to cloud-based HPC platforms (e.g., AWS ParallelCluster, Azure CycleCloud).
- Familiarity with AI/ML workflows and tools (e.g., TensorFlow, PyTorch) in GPU environments is a plus.
- Strong problem-solving, collaboration, and communication skills to support cross-functional teams.