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Rocket Lab is an end-to-end space company building rockets, spacecraft, and critical subsystems. The Optical Systems division solves mission-critical space domain and Intelligence, Surveillance, and Reconnaissance (ISR) challenges for Department of Defense and Intelligence Community customers, delivering innovative electro-optical and infrared systems for space, terrestrial, and airborne environments.
As a Senior Machine Learning Engineer II, you will support the U.S. Space Development Agency's Proliferated Warfighter Space Architecture (PWSA) Tranche 3 program by building deep learning neural networks for advanced Electro-Optical (EO/IR) image processing. You will design, train, and deploy machine learning models for optical systems applications, build and maintain ML pipelines for data ingestion, feature engineering, training, and inference, and collaborate with researchers and engineers on AI, machine learning, and computer vision solutions.
Key responsibilities include developing solutions for real-world space and defense problems, performing rapid prototyping and enhanced development for integration into operational systems, troubleshooting and maintaining existing and new systems, and staying current with ML research to evaluate new tools, frameworks, and techniques.
Required qualifications: Bachelor's degree with 8+ years of experience (or Master's with 6+ years, or Ph.D.), strong background in machine learning including model selection, architecting, training, validation, testing, and deployment. You must have experience building deep learning neural networks for computer vision, image processing, or video analysis (object detection, image segmentation, tracking), strong math background in linear algebra, proficiency in Python, experience with deep learning libraries (Keras, TensorFlow, PyTorch), and software engineering fundamentals (version control, testing, CI/CD, containerization). U.S. citizenship is required due to program requirements, and ability to obtain and maintain a U.S. Government Security Clearance is essential.
Nice-to-have skills include proficiency in C/C++/Rust, experience curating quality real-world datasets for training deep learning models, and existing TS/SCI security clearance.
