Riverside Research

Machine Learning Engineer

• 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

FPGA AI/ML Engineer – Part Time