Summary We are building a tightly scoped computer vision MVP for an internal product. This project is focused on analyzing pre-recorded workout videos to demonstrate core technical capability for identifying people, classifying exercises, and counting repetitions using pose estimation. This is an applied engineering project, not academic research and not a production deployment. The initial output will be used internally for demonstration and board review. What you will work on: You will build a Python-based pipeline that processes workout video files we provide and outputs structured analysis results. Specifically, the system should: • Identify known individuals in video clips using facial embeddings (closed-set recognition) • Classify the exercise being performed from a small, fixed set of exercises • Count repetitions for at least one exercise (for example squats) using pose-based heuristics • Output results in a structured format including identified user, exercise label, rep count, per-rep timestamps, and confidence scores A simple script or lightweight demo output is sufficient. UI polish is not a focus. Scope constraints: • Pre-recorded video only (no live streaming) • Single primary subject per clip • Small known user set (we will provide enrollment images) • Limited exercise set (approximately 5–8 exercises) • Rep counting required for at least one exercise • Python implementation • Cloud or local GPU friendly (mobile or on-device optimization is out of scope) Deliverables: • Python code implementing the full video analysis pipeline • Clear separation between face identification, exercise classification, and rep counting logic • Sample outputs on provided video files • Short README explaining how to run the pipeline and describing the architecture Required experience: • Strong Python skills • Computer vision fundamentals • Experience with video processing and OpenCV • Experience with pose estimation • Experience with deep learning frameworks such as PyTorch or TensorFlow • Ability to design clean, modular systems Nice to have: • Facial recognition or embedding-based identity systems • Human activity or action recognition experience • Prior work in fitness, sports, or biomechanics • GPU inference or model optimization experience Engagement details: • Start on Monday (February 23) • Duration approximately 4–6 weeks • Hourly contract Briefly describe how you would approach pose-based repetition counting for a squat using video. When applying, please include links to relevant computer vision or machine learning projects you have worked on.