AI Platform Engineer: Scale GPU AI & Research

Johns Hopkins Applied Physics Lab; Laurel (MD); On-site; USD 85,000 - 165,000; Full time

Job summary

Johns Hopkins Applied Physics Laboratory seeks an AI Solutions Engineer to bridge researchers and a scalable AI computing platform. You will configure and evolve the AI stack, collaborate on platform deployments, and assist researchers with GPU-accelerated workloads for training, inference, and deployment.

Ideal candidates have 3+ years in Kubernetes/Docker, hands-on Jupyter, and experience with PyTorch, TensorFlow, or similar frameworks.

Qualifications

• Bachelor of Science degree or equivalent professional experience.
• 3+ years of container-based orchestration (Kubernetes) and runtime environments (Docker).
• Hands-on experience with Jupyter environments and GPU-accelerated AI frameworks (PyTorch, TensorFlow, Hugging Face).
• Knowledge of Kubernetes and GPU computing for configuring and troubleshooting AI apps in shared environments.
• Experience supporting researchers with model training, fine-tuning, inference, and optimization.
• Proficiency in Python and shell scripting for automation and platform integration.
• Familiarity with ML workflows and research-to-production transitions.
• Strong problem-solving, communication, collaboration, and prioritization skills.
• Ability to obtain Interim Secret, eventually Secret clearance; U.S. citizenship required.

Responsibilities

• Own the AI platform configuration and evolution, adopting new capabilities and setting standards.
• Collaborate with Linux/Kubernetes admins on deployments, upgrades, and integration.
• Provide hands-on support to researchers using GPU-accelerated AI platforms for model work.
• Troubleshoot AI workloads across Python, Jupyter, containers, Kubernetes, and GPU components.
• Help scale workloads from single-GPU experiments to multi-GPU multi-node runs.
• Develop reusable environments, workflows, automation, and documentation to boost reliability.

Skills

Python

Shell scripting

GPU scheduling

AI frameworks

Distributed training

Research collaboration

Problem solving

Education

Bachelor of Science in CS or related field

Tools

Docker

Kubernetes

Jupyter

Job description

Johns Hopkins Applied Physics Laboratory seeks an AI Solutions Engineer to bridge researchers and a scalable AI computing platform. You will configure and evolve the AI stack, collaborate on platform deployments, and assist researchers with GPU-accelerated workloads for training, inference, and deployment.

Ideal candidates have 3+ years in Kubernetes/Docker, hands-on Jupyter, and experience with PyTorch, TensorFlow, or similar frameworks.