Cloud ML - Lead Engineer (2+)
qualcomm | 1 days ago | Bangalore

Responsibilities: 

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. 

Required Skills and Experience: 

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. 

Data Handling: Experience working with large datasets. 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. 

Communication: Excellent written and verbal communication skills. 

Work experience : Open, 4 – 10 years of relevant experience. 

 

Preferred Skills: 

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. 

 

Minimum Qualifications:

• Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
OR
Master's degree in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field and 1+ year of Software Engineering or related work experience.

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