Research Computing GPU Systems Engineer

Research Computing GPU Systems Engineer

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University IT (UIT), Stanford, California, United States Thank you for your interest in Stanford University. While we have instituted a hiring pause for non-critical staff positions, we are actively recruiting for the positions currently listed on our careers page.

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The application portal will be offline for maintenance from May 7 at 7 PM to May 8 at 3 AM (PT). Job Summary

DATE POSTED

5 hours ago

SCHEDULE

Full-time

JOB CODE

4834 EMPLOYEE STATUS Regular GRADE L REQUISITION ID 109145 WORK

ARRANGEMENT

Hybrid Eligible About the Role Stanford Research Computing seeks an exceptional GPU Cluster Lead Engineer to oversee technical operations, optimization, and strategic development of Marlowe, Stanford's NVIDIA SuperPOD. This role combines deep technical expertise in GPU computing, large-scale cluster management, and leadership in supporting a diverse research community. You will serve as the technical authority on GPU infrastructure, driving system performance and reliability while enabling groundbreaking research in AIML, computational biology, physics, and beyond. Key Responsibilities System Operations & Management Lead day-to-day operations of the GPU Cluster, ensuring optimal uptime and performance. Architect monitoring, alerting, and observability solutions using Prometheus, Grafana, DCGM, and Base Command Manager. Manage job scheduling and resource allocation using Slurm, implementing advanced GPU partitioning and configurations. Coordinate maintenance windows, system upgrades, and capacity expansions; lead incident response and root cause analyses. System storage management, optimization, benchmarking and observability reporting. Performance Optimization & Engineering Design performance tuning strategies for GPU utilization, job throughput, and system efficiency. Optimize

NVIDIA GPU

fabric configurations including NVLink, NVSwitch, and InfiniBand RDMA networking. Develop containerization strategies using NVIDIA NGC, Docker, and SingularityApptainer. Engineer solutions for deep learning frameworks (PyTorch, TensorFlow, JAX) and CUDA application optimization. Benchmark system performance and collaborate with NVIDIA on optimization programs. User Support & Research Enablement Serve as primary technical consultant for researchers using GPU-accelerated computing, Develop documentation, best practices guides, and training materials; deliver workshops on GPU computing workflows. Profile and optimize user workloads, scaling applications from single-GPU to multi-node distributed training. Team Leadership & Strategy Mentor junior engineers and contribute to strategic planning for GPU infrastructure expansion. Evaluate emerging GPU technologies and manage vendor relationships with NVIDIA and hardware suppliers. Represent SRC in ongoing interactions with the Stanford Data Sciences group on AIML infrastructure; participate in on-call rotation. Education & Experience Bachelor's degree in Computer Science, Engineering, or related field and ten years of relevant experience or a combination of education and relevant experience. 5+ years in HPC systems administration or research computing; 3+ years managing GPU clusters (NVIDIA

A100H100

) Required Qualifications Expert knowledge of

NVIDIA GPU

architecture, CUDA, and GPU computing principles (NVLink, MIG, GPUDirect) Advanced Linux administration (RHEL, Ubuntu); expertise with Slurm job scheduler Experience with high-performance networking (InfiniBand, RoCE) and parallel filesystems (Lustre, GPFS) Strong scripting (Python, Bash) and containerization experience (Docker, Singularity, Kubernetes) Familiarity with AIML frameworks (PyTorch, TensorFlow) and distributed training techniques Experience with monitoring tools (Prometheus, Grafana) and

NVIDIA DCGM

Preferred Qualifications Experience with Base Command Manager or Bright Cluster Manager Background in academic research computing or national lab environments Contributions to open-source HPC or GPU computing projects Knowledge of MLOps practices and GPU virtualization (vGPU, MIG) Key Competencies Technical leadership Creative problem-solving Excellent communication with technical and non-technical audiences Strong collaboration skills Service-oriented mindset Adaptability to rapidly evolving technology What We Offer Work with cutting-edge

NVIDIA GPU

technology enabling groundbreaking research Professional development opportunities Collaborative environment with talented engineers and researchers Comprehensive Stanford benefits package including health, dental, retirement, and education benefits Flexible work arrangements Physical Requirements•: Constantly perform desk-based computer tasks. Frequently sit, grasp lightlyfine manipulation. Occasionally standwalk, writing by hand. Rarely use a telephone, liftcarrypushpull objects that weigh up to 10 pounds. • Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form.

Working Conditions:

May work extended hours, evenings, and weekends.

Work Standards:

Interpersonal Skills:

Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.

Promote Culture of Safety:

Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned. Subject to and expected to stay in sync with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in Stanford's Administrative Guide, adminguide.stanford.edu. The expected pay range for this position is $190,577 to $200,000 per annum. Stanford University provides pay ranges representing its good faith estimate of the salary or hourly wage the university reasonably expects to pay for a position upon hire. The pay offered to a selected candidate will be determined based on factors such as (but not limited to) the scope and responsibilities of the position, the qualifications of the selected candidate, departmental budget availability, internal equity, geographic location and external market pay for comparable jobs. At Stanford University, base pay represents only one aspect of the comprehensive rewards package. The Cardinal at Work website provides detailed information on Stanford's extensive range of benefits and rewards offered to employees. Specifics about the rewards package for this position may be discussed during the hiring process. The job duties listed are typical examples of work performed by positions in this job classification and are not designed to contain or be interpreted as a comprehensive inventory of all duties, tasks, and responsibilities. Specific duties and responsibilities may vary depending on department or program needs without changing the general nature and scope of the job or level of responsibility. Employees may also perform other duties as assigned. Consistent with its obligations under the law, the University will provide reasonable accommodations to applicants and employees with disabilities. Applicants requiring a reasonable accommodation for any part of the application or hiring process should contact Stanford University Human Resources by submitting a contact form.