• Design, implement, and optimize FPGA logic using AMD/Xilinx toolchains (Vivado, Vitis, HLS) development in VHDL/Verilog • Integrate FPGA designs into larger systems, ensuring robust verification, documentation, and deployment across multiple platforms (Zynq, UltraScale+, Versal) • Develop innovative machine learning and computer vision solutions to analyze and exploit large, complex datasets from remote sensing phenomenology • Develop algorithms and associated software tools using C/C++/Python and associated machine learning libraries (PyTorch, LibTorch) • Train AI/ML models and tune their hyperparameters for a given dataset and algorithm objectives • Provide solutions for data collection and data linting that enable rapid, automated curation of training data • Keep up with the SoTA practices for AI/ML • Adhere to teams’ standards for reviewing source code, unit-testing, source code control, and documentation practices • Utilize Python PEP8 standards.