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AI​/ML Engineer II

Job in Annapolis Junction, Howard County, Maryland, 20701, USA
Listing for: Reflexive Concepts
Full Time position
Listed on 2026-06-27
Job specializations:
  • IT/Tech
    Machine Learning/ ML Engineer, AI Engineer (Applied/Software), Data Engineering, Data Scientist
  • Engineering
    AI Engineer (Applied/Software), Data Engineering
Job Description & How to Apply Below
Location: Annapolis Junction

Artificial Intelligence/Machine Learning Engineer

Reflexive Concepts is seeking a skilled Artificial Intelligence/Machine Learning Engineer to join our team! Specifically, we are looking for an AI/ML Engineer to design, create, test, and productize AI/ML algorithms in support of the FFPP VISTA Development and Sustainment (MT&E Team) mission. The AI/ML Engineer should be proficient in model architecture, data pipeline interaction, and metrics application, and familiar with application development, infrastructure management, data engineering, and data governance.

Qualifications:

  • Five (5) years experience in applied machine learning in programs and contracts of similar scope, type, and complexity
  • A Master's or Ph.D. degree in advanced math, artificial intelligence, data science, computer science or deep learning from an accredited college or university. Five additional years of machine learning experience with a relevant Bachelor's degree may be substituted for a Master's degree.
  • Experience with standard machine learning frameworks, e.g. PyTorch, Tensor Flow

Required:

  • Select appropriate data sets
  • Perform statistical analysis
  • Run machine learning algorithms
  • Use results to improve models
  • Train and retrain systems when needed
  • Experience working with various ML libraries and packages
  • 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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