Artificial Intelligence Scientist

at Chenega Corporation
Published December 3, 2022
Location Silver Spring, MD
Category Default  
Job Type Full-time  


Come join a company that strives for Extraordinary People and Exceptional Performance! Chenega Professional & Technical Services, LLC., a Chenega Professional Services’ company, is looking for a Artificial Intelligence Scientist I to support the FDA's Center of Devices and Radiological Health (CDRH), Office of Science and Engineering Laboratories (OSEL), and Division of Imaging, Diagnostics, and Software Reliability (DIDSR) in developing novel methods to identify and measure the representation and diversity of the data, its influence on the complex learning processes of AI-enabled devices, and to mitigate any possible disparities with particular emphasis on sex-related disparities.

In this role, the Artificial Intelligence Scientist I will provide research services for the FDA's CDRH, OSEL, and DIDSR Artificial Intelligence/Machine Learning Program at all stages of the project to: “Development of novel AI/ML models and an evaluation framework to mitigate algorithmic bias in static and dynamic environments.”

Our company offers employees the opportunity to join a team where there is a robust employee benefits program, management engagement, quality leadership, an atmosphere of teamwork, recognition for performance, and promotion opportunities. We actively strive to channel our highly engaged employee’s knowledge, critical thinking, and determination to innovate scalable solutions for our clients.


  • Perform data preparation, pre-processing, and harmonization from multiple sources to be used as predictors variables into a machine learning algorithm.
  • Create benchmark data to artificially simulate concept drift that could lead to catastrophic forgetting between data batches for training continual learning algorithms.
  • Develop methods to quantify diversity in the data and the effects on the machine learning models.
  • Develop a new evaluation approach and experimental simulations for performance assessment of continual learning algorithms
  • Develop imaging-based segmentation and classification models using traditional and/or deep-learning methods with varying levels/sources/distributions of data and compare model performance.
  • Organize, interpret and summarize data in a high quality, timely, accurate and detailed reports, suitable for publication(s), standard test methods and/or FDA guidance development.


  • A PhD or master’s degree in engineering, physics, computer science, mathematics, or a similar quantitative field.
  • Academic courses in Engineering, Physics, Optics, Mathematics, Computer Science, Statistics or similar
  • Ability to obtain a Public Trust government clearance.
  • Ideal candidate would have a strong background in the fundamentals of medical imaging-based artificial intelligence and machine learning methods and data analysis techniques and an eagerness to solve technical challenges systematically with experimental and/or computational approaches.
  • Experience with the development and analysis of traditional and deep-learning-based AI/ML methods (CNN, RNN, GAN, etc.)
  • Programming experience with Python (including scientific stack: NumPy, SciPy, scikit-learn, etc.), and deep learning frameworks (TensorFlow, PyTorch, etc.)
  • Experience with image segmentation, processing and data management
  • Experience analyzing and summarize scientific data for scientific interpretation and publication
  • Required to adhere to all government agency guidelines as it applies to COVID vaccine Attestation/Restrictions