2026 Graduate - Synthetic Aperture Radar ML Engineer - Imaging Systems

About the role

We are seeking an entry-level engineer or scientist to support the growth of RF/machine learning capabilities at the Johns Hopkins University Applied Physics Laboratory (APL).

Responsibilities

• Support development and optimization of SAR-related algorithms and processing workflows for execution on GPU-enabled edge hardware.
• Aid in profiling, debugging, and improving computational performance to meet edge-device constraints such as latency, memory, throughput, and power.
• Build, organize, and maintain curated datasets for machine learning training, validation, and testing.
• Develop and apply data preprocessing, labeling, and quality-check workflows to prepare data for analysis and model development.
• Train, evaluate, and help refine machine learning models for deployment in edge or resource-constrained environments.
• Support integration and deployment of algorithms and trained models onto edge computing platforms.
• Collaborate with senior staff to transition prototypes into robust, testable implementations.
• Document technical approaches, results, implementation details, and performance tradeoffs.
• Contribute to the team’s emerging RF/ML capabilities through applied development, experimentation, and technical learning.

Qualifications

• Bachelor’s or Master's degree in Electrical Engineering, Computer Engineering, Computer Science, Applied Mathematics, Physics, or relevant field.
• Foundation in signal processing, linear algebra, and related applied mathematical methods.
• Programming experience in Python, C++, or similar languages for technical computing, data processing, or algorithm development.
• Familiarity with basic machine learning workflows, including data preparation, model training, evaluation, and performance assessment.
• Ability to work with raw and processed data to create organized, curated datasets for analysis and model development.
• Experience with GPU programming, accelerated computing, or performance optimization tools and frameworks.
• Familiarity with machine learning frameworks such as PyTorch, TensorFlow, or similar toolkits.
• Experience with data curation, labeling, preprocessing, or dataset management for machine learning applications.
• Experience working in Linux-based development environments.

Benefits

The Johns Hopkins University Applied Physics Laboratory offers a comprehensive benefits package including retirement plans, paid time off, medical, dental, vision, life insurance, short-term disability, long-term disability, flexible spending accounts, education assistance, and training and development.

Pay

Minimum Rate $85,000 Annually
Maximum Rate $165,000 Annually

Schedule

Not specified