Overview Work within our clients machine learning team to deploy and optimize models for applications like low:latency speech recognition and large language models (LLMs). Initial focus will be on improving our clients speech recognition model's training pipeline on multi:GPU systems to boost performance and quality. Responsibilities: :Train and deploy state:of:the:art ML models. :Apply optimization techniques (distillation, pruning, quantization). :Enhance speech models with features such as diarization, multilingual support, and keyword boosting. :Optimize models for low:latency inference on accelerators. :Improve training workflows and GPU utilization. :Use data augmentation to improve performance. :Stay updated on ML research to guide strategy. Requirements: :Master's or PhD in a relevant field with strong ML foundations. :Training ML models for production use. :PyTorch or TensorFlow. :Handling large datasets (multi:terabyte). :Familiarity with Linux, version control, and CI/CD systems. :Knowledge of model compression (e.g., reduced precision).