GPU Systems Engineer 4

LocationBethesda, Maryland

Security clearanceTop Secret/SCI

Work arrangementOn-Site

Requisition5210

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Position Summary

Support enterprise AI mission systems by designing, developing, and optimizing GPU clusters, with deep focus on operating systems, hardware, GPU platforms, and high-speed networking in a secure customer environment.

Essential Duties and Responsibilities

• Design, configure, and maintain GPU clusters.
• Collaborate with a multidisciplinary team to define and optimize architectures for performance, power efficiency, and required features.
• Work closely with AI/ML engineers to integrate GPUs with Linux-based systems.
• Optimize GPU drivers for compatibility, reliability, and performance.
• Analyze GPU performance, identify bottlenecks, and develop strategies to improve efficiency across hardware and software layers.
• Build and maintain debugging tools, profiling utilities, and performance analysis software for Linux environments.
• Leverage Bash, Python, Ansible, Puppet, and Salt for tooling and automation.
• Maintain technical documentation, architectural specifications, and Linux best practices.
• Support ATO activities and ensure compliance with federal security standards.

Required Qualifications

• Active TS/SCI with ability to obtain a CI Polygraph.
• Bachelor's degree with a minimum of ten years of experience in the category field.
• Experience managing NVIDIA GPU data center platforms, including DGX, HGX, H200, H100, and L4s.
• Knowledge of enterprise server components, including storage/network controllers, HBAs, and SSDs.
• Strong expertise with Linux distributions, including RHEL, Ubuntu, Oracle, and Rocky.
• Excellent problem-solving skills and the ability to collaborate within a team.
• Meet DoD 8570.11 IAT Level II certification requirements at a minimum; IAT Level III is also acceptable.
• U.S. citizenship is required due to the nature of the government contracts supported.

Preferred Qualifications

• Experience with Kubernetes cluster management and AI/ML workflow orchestration, including Argo, Airflow, and Kubeflow.
• Familiarity with GPU virtualization and cloud computing.
• Experience with Prometheus and Grafana for monitoring.
• Knowledge of distributed resource scheduling systems such as Slurm, LSF, or similar tools.

Required Education and Experience Equivalency

• Bachelors' Degree with 10 years of experience.
• Masters' Degree with 10 years of experience.
• PhD with 10 years of experience.

Required Certifications

• DoD 8570.11 IAT Level II certification: Security+ CE, CCNA-Security, GICSP, GSEC, or SSCP.

Required Security Clearance

• Active TS/SCI with ability to obtain a CI Polygraph.