Computer Vision Engineer

Role Overview

Computer Vision / Deep Learning Scientists collaborate with cross-functional product teams across multiple business units to identify high-impact business problems and deliver scalable, data-driven solutions. This role involves designing, developing, and deploying novel computer vision and deep learning models, as well as evaluating model performance and data quality throughout the solution lifecycle.

The ideal candidate is highly technical, research-oriented, and collaborative, with strong experience in modern deep learning frameworks and computer vision techniques.

• Partner with product and business teams to define and solve complex, high-value computer vision problems
• Design, develop, and implement deep learning and computer vision models for unique real-world use cases
• Evaluate model accuracy, performance, and data quality; iterate to improve outcomes
• Conduct research to stay current with emerging algorithms, tools, and best practices in computer vision and deep learning
• Implement and optimize models for GPU-based training and inference, including parallel execution on GPU clusters
• Contribute to the full model lifecycle, from experimentation and prototyping through deployment and monitoring

• Strong proficiency in Python
• Hands-on experience with deep learning frameworks such as Tensor Flow, Keras, and/or Py Torch
• Experience with deep learning–based image processing techniques, including:
• Image classification
• Familiarity with common CNN architectures (e.g.,
  VGG
  16, Res Net, Mobile Net
  )
• Experience with traditional computer vision and image processing techniques using OpenCV
  , scikit-image (skimage), or similar libraries
• Experience training and running models on GPU-enabled systems and GPU clusters

• Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Machine Learning, Statistics
  , or a related field
• Level-appropriate experience (Junior, Intermediate, or Senior) in computer vision and/or deep learning roles