Sports Computer Vision Engineer

Build and train CV models for sports video: player/ball detection, multi-object tracking, pose/keypoints, event/action recognition, identity association (re-ID).

Own the experimentation loop: hypotheses → ablations → error analysis → measurable improvements.

Design and maintain evaluation: task-appropriate metrics (e.g., MOT metrics, keypoint accuracy, event precision/recall), dataset slices, and failure taxonomy.

Improve data efficiency: augmentations, sampling strategies, handling label noise, weak/self-supervision where helpful.

Prototype and iterate on modern architectures (e.g., transformer-based detection/tracking, temporal models, multi-task setups).

Collaborate on dataset + labeling design: formats, schemas, tooling, versioning.

Help productionize models: packaging, batch/stream inference patterns, throughput/latency tradeoffs, robustness checks.

Add lightweight quality gates: reproducibility, automated eval, regression detection

Sports video CV or adjacent domains (multi-agent tracking, pose, crowded scenes).