Responsibilities

• Model Training and Fine-tuning: Train LLMs from scratch using various datasets. Fine-tune pre-trained models on specific tasks or datasets to improve performance. Implement state-of-the-art LLM training techniques such as Reinforcement Learning from Human Feedback (RLHF), ZeRO (Zero Redundancy Optimizer), Speculative Sampling, and other speculative techniques.
• Data Management: Handle large datasets effectively. Ensure data quality and integrity. Implement data cleaning and preprocessing techniques. Hands-on with EDA is a plus.
• Model Evaluation: Evaluate model performance using appropriate metrics. Understand the trade-offs between different evaluation metrics.
• LLM metrics: Sound understanding of various LLM metrics like MMLU, Rouge, BLEU, Perplexity etc. AWQ: Understanding of Quantization is a plus. Knowledge on QAT will be a plus.
• Research and Development: Stay updated with the latest research in NLP and LLMs. Implement state-of-the-art techniques and contribute to research efforts.