FPGA AI/ML Engineer (Part time)

Riverside Research Institute

About The Position

Riverside Research is seeking a part time Artificial Intelligence / Machine Learning Engineer with expertise in real-time processing and FPGA development. As a valued Riverside Research employee, you will be part of a highly skilled and integrated team that analyzes intelligence data and develops automation solutions to challenging Scientific & Technical problems. All Riverside Research opportunities require U.S. Citizenship.

Requirements

TS/SCI clearance.
Bachelors’ degree in either Computer Engineering, Electrical Engineering, Mathematics, Statistics, Physics, Computer Science, or related field of study
Twelve years’ experience with FPGA development
Seven years' experience with computer vision and/or AI/ML R&D algorithm development
Experience with Git version control, branches, and merge conflict resolution
Proficient in collaborative Office 365 tools such as MS Word, Excel, and PowerPoint
Ability to work closely with subject-matter experts to develop tools, algorithms, and datasets needed for developing relevant and useful AI/ML prototype algorithms
Self-driven, strong analytic, inferencing, critical thinking, and creative problem-solving skills
Communicates highly technical results and methods clearly and succinctly

Nice To Haves

Advanced degree (MS/PhD) in Data Science, Mathematics, Statistics, Computer Science, a Physical Science or Engineering is strongly desired
Experience with DoD intelligence production processes and workflows
3+ years operational experience in radar signal processing analysis, overhead imagery analysis, orbital mechanics, and/or electronic warfare data analysis
2+ years experience using data visualization tools and libraries in Python
Experience with large (1 GB +) image data and formats such as HDF5, JSON, GEOTIFF, TFRecords, etc.
Experience in development of distributed, web-based systems, service-oriented architectures, front-end user interfaces, and back-end databases are a plus
Experience with interpretability of deep learning computer vision models including visualization and reasoning about model latent spaces and activation maps to assess model effectiveness / weaknesses
Familiarity in differences of supervised learning vs. unsupervised learning techniques

Responsibilities

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