JOB AD:
- OverviewWork 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).