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Captivation Software

Artificial Intelligence Machine Learning Engineer 2

Annapolis Junction, MD$130k–$270kmidAdded 3 days ago

About this role

Captivation Software seeks a mid-level AI/ML engineer to design, develop, and operationalize machine learning algorithms addressing defense and intelligence challenges. The role combines algorithm development with production deployment, requiring both technical depth and system integration expertise.

What you'll do

  • Design and implement AI/ML algorithms, then transition them from prototype to production-ready systems
  • Curate datasets, perform statistical analysis, and run machine learning models to solve business problems
  • Conduct iterative testing and validation of algorithms to meet verification requirements
  • Oversee system integration and ensure data/model output quality through monitoring and retraining
  • Research and evaluate a broad range of ML libraries, tools, and algorithmic approaches
  • Identify data distribution shifts and other factors degrading model performance in deployed systems

What they're looking for

  • Machine learning algorithms and statistical analysis
  • ML libraries and packages (e.g., TensorFlow, scikit-learn, PyTorch)
  • Data preprocessing, feature engineering, and data representation methods
  • Software architecture and data structures
  • Model testing, evaluation, and validation protocols
  • Data quality assessment and verification
  • Scalable system design and productionization
  • Problem-solving and algorithm optimization
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Captivation Software

Captivation Software builds high-performance computing and secure analytics infrastructure for mission-critical government programs, specializing in HPC testing, enterprise dataflow architectures, and cleared defense systems. The company is hiring senior test engineers, systems engineers, and technical leaders with deep expertise in Linux, cloud infrastructure, and security clearances to validate systems and architect solutions for intelligence community operations.

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Likely interview questions

  • Walk us through a machine learning project where you moved a prototype from research to production—what were the biggest challenges?
  • How do you approach selecting appropriate datasets and handling data distribution shifts that affect model performance?
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