Machine Learning Engineer, Computer Vision (NXJ-218)
The Role
The video understanding pipeline—from multi-object tracking and re-identification to action recognition—is yours to own and scale. The technical challenge lies in balancing high-throughput, low-latency GPU inference with cutting-edge detection accuracy across massive streams of sports footage. You will establish rigorous benchmark suites and decide which vision foundation models make it into production.
About the Product
The platform ingests live and recorded sports video to automatically extract real-time metadata, temporal key moments, player statistics, and scene-boundary events. Operating across multiple sports leagues, it relies on frame-accurate video parsing to power downstream broadcast automation, search, and analytics tooling.
Technology Stack: The core ML pipeline is built in Python using PyTorch for model training and fine-tuning. Production GPU inference is optimized for cost and latency using TensorRT and ONNX Runtime within Dockerized, queue-driven microservices. FFmpeg handles large-scale video decoding, while Azure (Service Bus, Blob Storage) underpins the asynchronous messaging infrastructure.
