AI Solutions Engineer

Do you thrive in a fast-paced, dynamic environment?

Do you have a passion for AI systems and computing platforms that enable them?

Are you a continuous learner who enjoys solving complex technical problems and working collaboratively with researchers, developers, and infrastructure teams? If so, we're looking for someone like you to join our team at APL.

TheAI Solutions Engineer combines AI platform engineering with hands-on researcher enablement. You will serve as a technical bridge between researchers and the organization's scalable AI computing platform. You will also work directly with researchers to move AI workloads from experimentation to large-scale GPU execution, providing expertise across Jupyter, containers, Kubernetes, GPU scheduling, distributed training, and AI frameworks.

• Own the technical configuration and evolution of the AI platform by evaluating new capabilities, determining appropriate adoption, and establishing application-level configurations, standards, and best practices.
• Partner with Linux and Kubernetes administrators on platform deployments, upgrades, infrastructure integration, and troubleshooting issues that span the AI platform and underlying computing environment.
• Provide hands-on technical assistance to researchers and data scientists using GPU-accelerated AI/ML platforms for model development, training, fine-tuning, and inference.
• Troubleshoot AI workloads across the stack, including Python environments, Jupyter, containers, Kubernetes, GPU scheduling and allocation, storage, networking, and AI frameworks.
• Help researchers optimize GPU utilization and scale workloads from interactive or single-GPU experimentation to multi-GPU and multi-node distributed execution.
• Develop and maintain reusable environments, workflows, automation, documentation, and operational guidance that improve platform reliability, usability, and researcher productivity.

You’ll meet our minimum qualifications if you…

• Hold a Bachelor of Science degree or equivalent years of related professional work experience.
• Have at least three (3) year experience using container-based orchestration (e.g. Kubernetes) and run-time environments (e.g. Docker).
• Have hands- on experience with Jupyter environments and GPU-accelerated AI frameworks such as PyTorch, Tensor Flow, Hugging Face, or similar technologies.
• Have working knowledge of Kubernetes and GPU computing concepts sufficient to configure, integrate, and troubleshoot AI applications running in shared computing environments.
• Have experience supporting researchers or developers with model training, fine-tuning, inference, workload optimization, and troubleshooting.
• Have proficiency with Python and shell scripting for automation, troubleshooting, and platform integration.
• Have working knowledge of machine learning concepts and model development workflows.
• Can demonstrate strong problem-solving, communication, collaboration, prioritization, and continuous-learning skills.
• Are able to work effectively with leadership to prioritize competing tasks.
• Are able to obtain Interim Secret level security clearance by your start date and can ultimately obtain Secret level clearance. If selected, you will be subject to a government security clearance investigation and must meet the requirements for access to classified information. Eligibility requirements include U.S. citizenship.

• Experience with AI workload orchestration and GPU scheduling platforms such as Run:ai, Slurm, Kubernetes-based GPU scheduling, or equivalent technologies.
• Experience with NVIDIA GPU platforms and technologies such as CUDA, NVIDIA GPU Operator, NCCL, or NVIDIA Container Toolkit.
• Experience supporting distributed…