In this role, the Model Optimization Engineer will be an expert in understanding the internal workings of PyTorch, graph capturing and graph editing mechanisms, methods to observe and modify intermediate activations and weights, tensor subclasses, custom ops, different types of parallelism for training models, and use this knowledge to implement and update the core infrastructure of the optimization library which enables an efficient and scalable implementation of various classes of compression algorithms. You'll also set up and debug training jobs, datasets, evaluation, performance benchmarking pipelines. Additionally, you will...- Design and develop the core infrastructure which powers the implementations of various compression algorithms (training time, post training, data free, calibration data based etc)- Implement the latest algorithms from research papers for model compression in the optimization library.- Design clean, intuitive, maintainable APIs - Run detailed experiments and ablation studies to profile algorithms on various models and tasks, across different model sizes.