GPU Graphics Process Unit Engineer
Leidos
- Bethesda, MD
- Permanent
- Full-time
- Bachelor's or higher degree in Computer Science, Electrical Engineering, or a related field. Additional years of experience may be considered in lieu of a degree.
- 10+ years of relevant systems engineering experience
- Proven experience in GPU architecture design, and GPU performance optimization.
- Expertise in operating system integration for Linux.
- Strong understanding of computer hardware architecture, particularly as it relates to 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 and the ability to collaborate within a team.
- Strong communication skills for conveying technical information in a Linux context.
- Proficiency with scripting languages such as Python or BASH.
- Proficiency with automation tools such Ansible, Puppet, Salt, Terraform, etc.
- Candidate must, at a minimum, meet DoD 8570.11- IAT Level II certification requirements (currently Security+ CE, CCNA-Security, GICSP, GSEC, or SSCP along with an appropriate computing environment (CE) certification). An IAT Level III certification would also be acceptable (CASP+, CCNP Security, CISA, CISSP, GCED, GCIH, CCSP).
- TS/SCI clearance with Polygraph required OR TS/SCI and willingness to get a Poly.
- US Citizenship is required due to the nature of the government contracts we support.
- Published research or contributions in the GPU industry, especially 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 in the field.
- 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 (preferred), LSF, etc.]
- Familiarity with CUDA and managing GPU-accelerated computing systems
- Basic knowledge of deep learning frameworks and algorithms