Machine Learning Engineer (Intelligence & Sensing)
- Candidate must be a U.S. Citizen and possess (or be eligible to obtain) and maintain a SECRET clearance.
- Master’s degree in Computer Science, Electrical/Computer Engineering, Applied Mathematics, Physics, or a related STEM discipline.
- Strong proficiency in Python, with demonstrated experience developing and optimizing computer vision algorithms for real-time execution on embedded and edge platforms.
- Advanced understanding of computer vision and machine learning methods for object detection, object tracking, and object recognition,
- Proficiency with deep learning frameworks such as PyTorch or TensorFlow/Keras, including experience modifying or extending framework components to support custom vision pipelines and specialized hardware accelerators.
- Experience with embedded vision systems, including low-level optimization in C/C++ or assembly, and integration of perception algorithms with sensor data streams (e.g., EO/IR, video, or multimodal inputs).
- Proven track record of deploying computer vision and target tracking models into production environments, particularly on resource-constrained devices or distributed edge-compute architectures.
- Deep expertise in CUDA and/or OpenCL for accelerating image processing, neural network inference, and tracking pipelines on GPU-enabled systems.
- Experience with OpenGL and/or WebGL for real-time visualization of detections, tracks, sensor data, and algorithm outputs.
- Fluency working in Linux-based HPC and shared compute environments, with experience scaling training and evaluation of vision and tracking models across multiple GPUs or nodes.
- Experience collaborating within multidisciplinary engineering teams using Git-based version control workflows for code development, review, and integration.
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