Computer Vision Engineer
Aviso de fuente externaen Programming.com
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 spor...
Salario
No especificado
Ubicación
Mexico City, Mexico
Tipo de empleo
Tiempo completo
Modalidad
No especificado
Computer Vision Engineer
Mexico City, Mexico
Descripción del empleo
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 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).
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