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MORSE Corp

Machine Learning Engineer

Cambridge, MA, Arlington, VA, Seattle, WAFrom $210kmidAdded today

About this role

MORSE Corp seeks a Machine Learning Engineer to design and deploy sophisticated ML systems, with emphasis on computer vision and NLP for national security applications. You'll develop algorithms from research through production across multiple deployment environments, collaborating with a team of scientists and engineers on cutting-edge AI solutions.

What you'll do

  • Develop, fine-tune, and optimize computer vision algorithms for object detection, tracking, and related tasks
  • Manage ML lifecycle including data acquisition, algorithm integration, testing, and field test analysis
  • Implement MLOps practices for experiment tracking, data management, and reproducibility
  • Write robust, maintainable production code and transition algorithms to on-premise, cloud, or embedded systems
  • Monitor latest ML research and incorporate new methodologies into MORSE's solutions
  • Collaborate with engineering teams to operationalize advanced algorithms for mission-critical environments

What they're looking for

  • Computer vision and object detection/tracking
  • Python or similar ML programming languages
  • Deep learning frameworks (TensorFlow, PyTorch, etc.)
  • MLOps tools and practices
  • Algorithm optimization and testing
  • Large language models or NLP
  • Audio analysis or multimodal ML
  • Cloud and embedded systems deployment
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MORSE Corp

MORSE Corp develops advanced technologies for national security, including unmanned aerial systems, mission-critical software, and AI-enhanced defense solutions. The company is hiring manufacturing engineers, systems engineers, robotics software engineers, Python developers, and human factors specialists to scale production and advance capabilities across hardware, embedded systems, and AI applications.

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Likely interview questions

  • Describe your experience developing and optimizing computer vision models—what was your most challenging CV project?
  • How have you used MLOps tools to manage experiments and ensure reproducibility in your ML workflows?
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