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# Sports Computer Vision Engineer

- **Company:** SumerSports LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Architectural Patterns, Software Debugging, FFmpeg, Python (Programming Language), Management of Software Versions, Graphics Processing Unit (GPU), Pytorch, Machine Learning Operations, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/sports-computer-vision-engineer-sumersports-llc-8992684

## About the Role

Must-have:

 * Strong applied CV experience with hands-on model development (not just running existing repos).
 * Solid PyTorch skills: training loops, debugging, data pipelines for vision workloads, DDP basics.
 * Comfort with video CV fundamentals: occlusion, identity switches, temporal consistency, calibration, domain shift.
 * Strong Python engineering and a bias toward measurable outcomes.

Nice-to-have (Bonus):

 * Sports video CV or adjacent domains (multi-agent tracking, pose, crowded scenes).
 * Experience with video tooling (FFmpeg), efficient dataset formats (WebDataset/shards), or streaming/batching to GPUs.
 * MLOps/production experience: model packaging, CI for training/eval, serving (Triton/TorchServe), monitoring.

## Description

* 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).

Research that Ships

 * 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


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