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Deep Learning R&D Software Engineer At Expedition Technology (EXP), we work on real-world problems with solutions that impact national security. We seek an experienced Deep Learning and Software Engineer to join a dynamic team delivering innovative solutions to the Intelligence Community. The ideal candidate has expertise in DL architectures such as YOLOv8, Faster R-CNN, DETR, and other state-of-the-art object detection models. Responsibilities include developing end-to-end deployable systems, sourcing and curating datasets, training and evaluating models, and productizing deliverables. You will also develop ML pipelines for Geospatial Object Detection, apply Sim-to-Real ML techniques, design custom algorithms, and produce technical reports. Experience in developing models against adversarial attacks on electro-optical imagery for real-time detection is required. The role involves building new deep learning algorithms, working on hard problems, and collaborating in a fast-paced, scientifically rigorous environment. Responsibilities include: Researching academic literature to stay current with latest techniques Developing deep learning architectures and models for novel applications Implementing solutions in Python (PyTorch) Delivering high-quality, tested software Participating in team brainstorming for innovative solutions Required Skills and Experience: US Citizenship with an ACTIVE Top Secret / SCI Clearance MS (3+ years) or BS (5+ years) in Computer Science, Electrical Engineering, Physics, Statistics, or Applied Mathematics Proficiency in PyTorch, TensorFlow, or similar frameworks Solid understanding of deep neural networks and applications in computer vision Experience with ML solutions in AWS environment Proficiency with Git and collaborative software development Strong communication skills Software development skills in Python and other languages Experience with data structures, algorithms, CI/CD, agile methodologies, and testing Familiarity with Docker, Linux, and cloud development Clearance Required:

Top Secret/SCI #J-18808-Ljbffr