Computer Vision Engineer

2 weeks ago

Remote, South Africa StatsProAI Full time

Overview We’re building next-generation sports analytics for rugby — using video AI to detect, classify, and track game events like scrums, lineouts, and attacking phases. You’ll lead the computer-vision side of our stack: developing event-detection models, training feature-extraction networks, and supporting real-time inference on the cloud. This is a hands-on,role suited to someone who enjoys shipping models that run at production scale and is excited about learning the full CV development and deployment stack. Key Responsibilities Develop and train models for rugby event and phase detection (e.g. scrums, lineouts, open-play transitions). Design and implement pipelines for data curation, augmentation and management. Build and optimise embeddings for player tracking and team identification. Implement temporal action localization and weakly-supervised learning techniques. Evaluate model accuracy and latency trade-offs; export to ONNX / TensorRT for real-time inference on GCP. Collaborate with the MLOps/Backend engineer to define the input–output schemas for APIs and ensure end-to-end deployment readiness. Required Skills Languages: Python (core), basic SQL. Frameworks: PyTorch (preferred), TorchVision, PyTorch Lightning. Libraries / Tools: NumPy, pandas, scikit-learn, OpenCV, FFmpeg, Matplotlib, Seaborn, Plotly, FiftyOne. Deployment Tooling: ONNX Runtime. Video Dataset Management: Proven experience working with large-scale video datasets (hundreds to thousands of clips) for training and validatio. Skilled in dataset curation, cleaning, and augmentation — balancing, filtering, temporal sampling, and clip segmentation Version Control: Git / GitHub; experience with experiment tracking (Weights & Biases, MLflow). Strong understanding of modern computer-vision and video-understanding techniques. Familiarity with object detection, Re-ID, and tracking pipelines (e.g. DeepSORT, ByteTrack). Nice-to-Have Background or strong interest in rugby / team sports analytics. Experience with weakly-supervised or self-supervised representation learning. Practical exposure to multi-camera or multi-view datasets (e.g., broadcast + tactical angles in sports) Experience using or integrating annotation and dataset-management tools, such as: CVAT, Label Studio, Supervisely, V7, or FiftyOne for visualization and active learning loops Familiarity with Roboflow, Weights & Biases Artifacts, or ClearML for dataset versioning and experiment tracking Profile Seniority: Junior-Mid (3–6 years experience). Engagement: Full-time or 4–5 days/week. Location: Remote (South Africa).

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