Computer Vision Engineer

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 exist‐ ing models and pipelines, evaluate performance on new datasets, identify gaps, implement improvements, and partner with engineering teams to deliver production-ready solutions.

REQUIRED QUALIFICATIONS:

• Strong hands-on experience building and improving computer vision systems. Proficiency with Python and modern machine learning frameworks such as PyTorch.
• Experience working with video-based computer vision problems, including detection, track‐ ing, event recognition, or identity association.
• Working knowledge of geometric computer vision: camera calibration, homography and pro‐ jective geometry, and mapping image coordinates to real-world coordinates.
• Experience evaluating model performance, identifying failure modes, and implementing practical improvements.
• Experience adapting models to challenging real-world data where video quality, camera an‐ gles, and environmental conditions vary significantly, including domain adaptation / transfer learning across different data distributions.
• Strong software engineering fundamentals and the ability to write maintainable, productionquality code.
• Ability to work independently, prioritize effectively, and drive projects to completion.

PREFERRED QUALIFICATIONS:

• Experience working with sports video or related domains (American football experience is a strong plus).
• Experience with large-scale video processing pipelines.
• Familiarity with tools such as FFmpeg and GPU-accelerated video workflows.
• Familiarity with OCR / scene-text recognition (e.g., reading jersey numbers or scoreboard graphics).
• Experience with experiment tracking and model/data versioning (e.g., Weights & Biases, MLflow, lakeFS/DVC).
• Experience deploying machine learning models into production environments.
• Experience with model monitoring, performance tracking, and operational support.
• Experience with human pose estimation (a forward-looking capability for this role).