Machine Learning Developer in Rocky Mountain House, AB, Canada

A Python-based cloud service for training and deploying natural language understanding and computer vision models for task-oriented conversational agents. I was responsible for developing the data science workflow, model, and data management as well as the production service. I was also responsible for implementing and training a number of models for image segmentation and classification, intent detection, sentiment, and entity extraction. The technologies used include PyTorch, TorchVision, TensorFlow, SKlearn, Pandas, swagger/OpenAPI, Flask, and PostgreSQL.

An image processing and machine vision framework implemented in C++, CUDA, and GLSL and optimized for real-time execution on multiple simultaneous high-resolution camera streams. The framework also included my own long-range and low light image enhancement algorithms, image stabilization, object detection, and target tracking implementations as well as software interfaces to hardware devices like machine vision cameras, pan-tilt-zoom systems, and communication radios.

A physically-based renderer for indoor scenes written in C++. The renderer used path tracing and other ray tracing variants to create simulated images of indoor and outdoor scenes for a surveillance modeling application. The implementation included area and skylights, various bidirectional scattering functions, and acceleration structures to support large complex scenes, as well as concepts such as flux, radiance, irradiance, and brightness.