Role: Senior Applied Computer Vision Engineer (Remote)
SUMMARY:
We are looking for a Senior Applied Computer Vision Engineer to help build and improve video intelligence solutions for sports. This role is focused on applying computer vision and machine learning techniques to real-world sports video workflows. You will work with existing models and pipelines, evaluate performance on new datasets, identify gaps, implement improvements, and partner with engineering teams to deliver production-ready solutions.
REQUIRED QUALIFICATIONS:
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Strong hands-on experience building and improving computer vision systems. Proficiency with Python and modern machine learning frameworks such as PyTorch.
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Experience working with video-based computer vision problems, including detection, tracking, event recognition, or identity association.
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Working knowledge of geometric computer vision: camera calibration, homography and projective geometry, and mapping image coordinates to real-world coordinates.
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Experience evaluating model performance, identifying failure modes, and implementing practical improvements.
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Experience adapting models to challenging real-world data where video quality, camera angles, and environmental conditions vary significantly, including domain adaptation / transfer learning across different data distributions.
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Strong software engineering fundamentals and the ability to write maintainable, production quality code.
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Ability to work independently, prioritize effectively, and drive projects to completion.
PREFERRED QUALIFICATIONS:
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Experience working with sports video or related domains (American football experience is a strong plus).
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Experience with large-scale video processing pipelines.
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Familiarity with tools such as FFmpeg and GPU-accelerated video workflows.
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Familiarity with OCR / scene-text recognition (e.g., reading jersey numbers or scoreboard graphics).
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Experience with experiment tracking and model/data versioning (e.g., Weights & Biases, MLflow, lakeFS/DVC).
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Experience deploying machine learning models into production environments.
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Experience with model monitoring, performance tracking, and operational support.
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Experience with human pose estimation (a forward-looking capability for this role).
