Senior Applied Computer Vision Engineer
New
J
Janea SystemsSports Analytics
Fully Remote/ European Residence requiredFull-TimeSenior
SalaryCompetitive, based on experience
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Job Details
- Required Skills
- PythonMachine LearningPyTorchSoftware EngineeringComputer Vision
Requirements
- Strong hands-on experience building and improving production-grade computer vision systems.
- Proficiency with Python and modern machine learning frameworks such as PyTorch.
- Experience with video-based computer vision problems, including object detection, multi-object tracking, event recognition, identity association, or video analytics.
- Strong working knowledge of geometric computer vision, including camera calibration, homography estimation, projective geometry, and mapping image-space detections to real-world 2D or 3D coordinates.
- Experience designing or improving tracking systems that handle occlusions, object interactions, identity preservation, noisy detections, and missing information.
- Experience evaluating model performance, identifying failure modes, and implementing practical improvements.
- Experience adapting models to challenging real-world data where video quality, camera angles, camera placement, and environmental conditions vary significantly.
- Experience with transfer learning, domain adaptation, data augmentation, and fine-tuning models on domain-specific datasets.
- Strong software engineering fundamentals and the ability to write clean, maintainable, production-quality code.
- Ability to work independently, prioritize effectively, and drive technical initiatives to completion.
- Strong communication skills and the ability to collaborate directly with clients and cross-functional engineering teams.
Responsibilities
- Develop and improve computer vision models for sports video, including player and ball detection, tracking, event recognition, and identity association.
- Build and improve camera calibration, homography, and field-registration solutions that map image coordinates into normalized field coordinates.
- Analyze existing computer vision pipelines, establish baselines, identify weak links, and recommend practical improvements.
- Improve tracking robustness across different stadiums, camera placements, broadcast styles, video qualities, and environmental conditions.
- Design experiments covering data acquisition, dataset creation, augmentation, model training, fine-tuning, evaluation, and deployment readiness.
- Analyze failure modes and implement improvements that increase accuracy, reliability, scalability, and robustness.
- Adapt existing models and pipelines to support new sports, leagues, camera configurations, and video sources.
- Partner with data teams on labeling workflows, dataset quality, validation processes, and human-in-the-loop improvement cycles.
- Work closely with software, platform, and DevOps engineers to deploy computer vision models and pipelines into production environments.
- Lead initiatives end-to-end, from technical discovery and prototyping through production deployment.
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