AI/ML Engineer II

Reflexive ConceptsAnnapolis Junction, MD

About The Position

Reflexive Concepts is seeking an AI/ML Engineer II to join our team. This role involves selecting appropriate data sets, performing statistical analysis, running machine learning algorithms, and using the results to improve models. The engineer will also train and retrain systems as needed, provide system integration oversight, and oversee the testing and evaluation of AI and ML algorithms through an iterative design process to meet verification and validation requirements. Additionally, the role includes researching and implementing a broad range of AI and ML algorithms and tools, designing or selecting appropriate data and knowledge representation methods, recognizing software architecture, data modeling, and data structures, and transforming data science prototypes into scalable solutions. The engineer will also be responsible for verifying data and model output quality and identifying differences in data distribution that affect model performance.

Requirements

  • Active TS/SCI with polygraph (in-scope, within 7 years)
  • Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity is required.
  • Experience with standard machine language frameworks, e.g. PyTorch, TensorFlow.

Responsibilities

  • Select appropriate data sets
  • Perform statistical analysis
  • Run machine learning algorithms
  • Use results to improve models
  • Train and retrain systems when needed
  • Run standard test and evaluation protocols
  • Provide system integration oversight
  • Oversee Test and evaluation of AI and ML algorithms through an iterative design process to meet verification and validation requirements
  • Research and implement a broad range of AI and ML algorithms and tools
  • Design or Select appropriate data and knowledge representation methods
  • Recognize software architecture, data modelling, and data structures
  • Transform and convert data science prototypes into scalable solutions
  • Verify data and model output quality
  • Identify differences in data distribution that affect model performance
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