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CaseGuard

Machine Learning Engineer - Computer Vision

Arlington, VAmidAdded 1 month ago

About this role

CaseGuard seeks a Computer Vision Machine Learning Engineer to develop and deploy AI models for image and video processing applications serving law enforcement, federal agencies, and other sectors. You'll optimize vision-based solutions, manage model lifecycles from training to production, and collaborate with cross-functional teams to integrate machine learning into business processes.

What you'll do

  • Design and deploy computer vision models for object detection, tracking, segmentation, and facial recognition
  • Optimize deep learning algorithms for real-time performance and scalability
  • Prepare and preprocess large datasets for model training and evaluation
  • Deploy models to production and monitor performance for reliability
  • Collaborate with software engineers and product teams on data-driven opportunities
  • Document implementations to ensure reproducibility and maintain code quality

What they're looking for

  • Deep learning frameworks (TensorFlow, PyTorch, Huggingface Transformers)
  • Python and C++
  • Image and video processing techniques
  • MLOps and cloud deployment (AWS, Azure, Google Cloud)
  • Data manipulation (Pandas, NumPy, SQL)
  • Model serving tools (ONNX, Triton, vLLM)
  • Machine learning algorithms and supervised/unsupervised learning
  • Docker and Kubernetes (preferred)

Benefits

  • Competitive salary
  • Stock options
  • Medical, dental, and vision insurance
  • 401(k)
  • Paid vacation and 10 paid holidays annually
  • Learning-focused and collaborative work environment
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CaseGuard

CaseGuard builds a C# WPF-based redaction platform with computer vision and machine learning capabilities for law enforcement and federal agencies. The company is hiring Software Engineers in Test, Machine Learning Engineers, and DevOps Engineers to strengthen its quality assurance, AI model development, and cloud infrastructure teams.

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Likely interview questions

  • Describe your experience with object detection and tracking models. Which architectures have you implemented, and how did you optimize them for real-time performance?
  • Walk us through your experience deploying machine learning models to production on cloud platforms like AWS, Azure, or Google Cloud. What deployment tools have you used, and how did you handle model serving?
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