Engineer, Senior Staff

Model architecture optimizations : optimize latest LLM and GenAI model architectures for NPUs, which involves reimplementing basic building blocks of models for NPUs Model Training and Fine-tuning: 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 up to date with the latest research in NLP and LLMs. Implement state-of-the-art techniques and contribute to research efforts. Infrastructure development : For coming up with new optimization techniques to minimize ONNX memory footprint, export time optimizations. Collaboration: Work closely with other teams to understand requirements and implement solutions. Deep Learning Frameworks: Hands-on experience with PyTorch at a granular level. Familiarity with tensor operations, automatic differentiation, and GPU acceleration in PyTorch. NLP and LLMs: Strong understanding of Natural Language Processing (NLP) and experience working with LLMs. Programming: Proficiency in Python and experience with software development best practices. Familiarity with data version control tools is a plus. Education: A degree in Computer Science, Machine Learning, AI, or related field. Advanced degree is a plus. Work experience : Open, 4 - 10 years of relevant experience. Exp - 9+ to 12 years Optimization: Knowledge of optimization techniques for training large models. Neural Architecture Search (NAS): Experience with NAS techniques for optimizing model architectures is a plus. Hands-on experience with CUDA, CUDNN and Triton-lang is a plus Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 6+ years of Software Engineering or related work experience. OR Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Software Engineering or related work experience. OR PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience. 3+ years of work experience with Programming Language such as C, C++, Java, Python, etc.