We are seeking a forward-leaning High Compute Engineer to lead the design, optimization, and integration of GPU-centric high-performance compute environments. The ideal candidate will be responsible for managing existing NVIDIA A100 and DGX-1 systems while designing scalable architectures to incorporate emerging GPU hardware as mission demands evolve. This role is critical to our advanced compute initiatives, where performance, stability, and future-readiness drive every architectural decision. You'll work cross-functionally with data scientists, AI/ML developers, cybersecurity experts, and infrastructure teams to create a robust, secure, and performant GPU compute ecosystem. This is a 100% on-site position. All work must be performed at the customer site in Bethesda at the Intelligence Community Campus. Responsibilities include managing, optimizing, and monitoring existing high-performance GPU systems including NVIDIA A100s and DGX-1 platforms; architecting integration plans for scaling GPU compute infrastructure, including newer platforms (e.g., H100, Grace Hopper, AMD Instinct); collaborating with data science teams to fine-tune GPU workloads for AI/ML pipelines; designing and implementing high-speed networking (InfiniBand/RDMA) and storage solutions optimized for GPU data flow; developing automation workflows using infrastructure-as-code (IaC) tools (e.g., Ansible, Terraform, SaltStack); ensuring system security, compliance, and patch management in alignment with NIST, RMF, or agency-specific controls; analyzing compute performance metrics and providing strategic recommendations for system enhancements; maintaining documentation on system architectures, configurations, and operational procedures. Requirements include a Bachelor's or higher degree in Computer Engineering, Computer Science, or a related field with at least 12 years of related technical experience (or additional years of experience in lieu of a degree); 5+ years experience supporting GPU compute environments in mission-critical or enterprise settings; proficiency with NVIDIA technologies: A100, DGX-1, CUDA, cuDNN, NCCL; strong background in Linux (RHEL/CentOS/Ubuntu), kernel tuning, and HPC stack deployment; experience with containerized GPU workloads using Docker, Kubernetes, and NVIDIA GPU Operator; familiarity with distributed compute frameworks (e.g., SLURM, Kubernetes, Ray); strong scripting skills: Bash, Python, or similar; proven ability to plan and execute large-scale system upgrades and migrations; must meet DoD 8570.11- IAT Level II certification requirements or IAT Level III certification; active TS/SCI clearance with Polygraph required OR active TS/SCI and willingness to obtain and maintain a Poly; US Citizenship is required. Preferred qualifications include experience with hybrid cloud GPU environments (AWS, GCP, or Azure with NVIDIA support); familiarity with AI/ML tooling such as PyTorch, TensorFlow, ONNX, and RAPIDS; experience integrating GPUs with storage systems (e.g., Lustre, BeeGFS, Ceph); exposure to hardware acceleration platforms (e.g., FPGA, custom ASIC).
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