2027 Graduate - Synthetic Aperture Radar ML Engineer - Imaging Systems

Description

Are you passionate about machine learning, edge processing, and synthetic aperture radar imaging?

Do you seek a position to apply your engineering skills in an innovative and collaborative laboratory environment?

If so, we are looking for someone like you to join our team at the Johns Hopkins University Applied Physics Laboratory (APL)!

We are seeking an entry-level engineer or scientist to support the growth of RF/machine learning capabilities. The selected candidate will contribute in two primary areas: optimization of SAR-related processing for GPU-enabled edge hardware, and support of machine learning workflows including curated dataset development, model training, evaluation, and deployment to edge devices. This role is intended for a candidate with strong technical fundamentals and the potential to grow into a broader RF/ML contributor through mentorship and hands-on experience. Deep SAR expertise is not required. As a member of our team, you will…* Support development and optimization of SAR-related algorithms and processing workflows for execution on GPU-enabled edge hardware. * Assist with 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. * Work closely with mentors and team members t