Vacancy expired!
Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. We provide high-end technical services, research and development, and prototype solutions to some of the country's most challenging technical problems.
Job Number: 1259Riverside Research is seeking a Senior Artificial Intelligence / Machine Learning Engineer to support existing contracts to prototype and develop automation solutions to NASIC's most difficult Scientific & Technical Intelligence problems. As a highly valued and sought-after Riverside Research employee, you will be part of a highly skilled and integrated team that analyzes intelligence data to discover opportunities for automated solutions. All Riverside Research opportunities require US citizenship. Job Responsibilities: AI/ML algorithm development for prototype applications in remote sensing:-
- Develop prototype AI/ML algorithms and associated software tools using Python and the Python/TensorFlow API
- Train AI/ML models and tune their hyperparameters for a given dataset and algorithm objectives
- Visualize hyperparameter optimization spaces with Tensorboard for selection of optimal parameters for a given parametric (functional) TensorFlow model
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- Provide customized solutions to data quality control that ensure accurate functional mappings for AI/ML algorithms on complex remote sensing datasets
- Develop databases / data lakes / data warehouses for organizing both structured and unstructured datasets
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- Apply machine learning and general computer vision best practices and methods to analyze and exploit large, complex remote sensing datasets from a variety of remote sensing phenomenology
- Keep up with the SoTA practices for AI/ML, perform relevant R&D, and implement new and innovative ideas in machine learning and high-performance computing to solve long-standing remote sensing "big-data" exploitation problems
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- Contribute and adhere to the AI team's standards for reviewing and unit-testing code, lead or participate in team-wide code reviews, and adhere to standardized documentation practices
- Utilize Python PEP8 standards
- US Citizen able to obtain and maintain a clearance (up to a TS/SCI minimum).
- Bachelor's Degree in either Electrical Engineering, Mathematics, Statistics, Physics, Computer Science, or related field of study
- 8+ years demonstrated proficiency in Python-based end-to-end AI/ML model development lifecycle using a recent deep learning platform (TensorFlow preferred)
- Awareness of version control, branches, merge conflict resolution, and git in general
- 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
- Advanced degree (MS/PhD) in Data Science, Mathematics, Statistics, Computer Science, a Physical Science or Engineering is strongly desired
- Active Secret Security Clearance
- Experience with DoD intelligence production processes and workflows
- 1+ years operational experience in radar signal processing analysis, overhead imagery analysis, orbital mechanics, and/or electronic warfare data analysis
- 1+ years experience using data visualization tools and libraries in Python
- Visualizations/Web Development Skills (e.g., Tableau, MEAN stack - MongoDB, ExpressJS, AngularJS, NodeJS)
- 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
Vacancy expired!