Senior GPU Systems Engineer

Description

Leidos is seeking a highly skilled Senior GPU Systems Engineer to join our team. This position involves designing, developing, and optimizing GPUs for various applications, with a focus on seamless integration with operating systems and hardware. The role also requires collaborating with a multidisciplinary team to define, develop, and optimize GPU architectures, integrating GPUs with Linux-based systems, contributing to hardware design, developing and optimizing applications using CUDA or OpenCL, analyzing GPU performance, developing testing and validation procedures, maintaining technical documentation, and staying updated on industry trends.

Basic Qualifications

• Bachelor's or higher degree in Computer Science, Electrical Engineering, or a related field. Additional years of experience considered in lieu of a degree.
• 10+ years of relevant systems engineering experience
• Expertise in GPU architecture design and performance optimization
• Proficiency in operating system integration for Linux
• Strong understanding of computer hardware architecture, particularly in Linux systems
• Knowledge of parallel computing, graphics algorithms, and real-time rendering in Linux environments
• Familiarity with GPU debugging tools and profiling software for Linux
• Excellent problem-solving skills
• Strong communication skills for conveying technical information
• Proficiency with scripting languages such as Python or BASH
• Experience with automation tools such Ansible, Puppet, Salt, Terraform, etc.
• DoD 8570.11- IAT Level II certification (currently Security+ CE, CCNA-Security, GICSP, GSEC, or SSCP) or IAT Level III certification (CASP+, CCNP Security, CISA, CISSP, GCED, GCIH, CCSP)

Clearance

• TS/SCI clearance with Polygraph required or TS/SCI and willingness to obtain a Poly
• US Citizenship required

Preferred Qualifications

• Published research or contributions in the GPU industry, particularly related to Linux
• Experience with machine learning and neural network frameworks on GPUs in Linux
• Knowledge of GPU virtualization, cloud computing, and emerging Linux-based technologies
• Proficiency in programming languages such as GPU-specific languages
• Experience with container technologies (Docker, Kubernetes)
• Experience with Prometheus/Grafana for monitoring
• Knowledge of distributed resource scheduling systems (Slurm, LSF, etc.)
• Familiarity with CUDA and managing GPU-accelerated computing systems
• Basic knowledge of deep learning frameworks and algorithms