Engineer, Senior Staff
Qualcomm · Bengaluru · 9+ yrs experience · Posted 2026-06-04
Tech stack: C, C++, Java, Python
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About the role
Qualcomm India Private Limited
Engineering Group, Engineering Group > Software Engineering
Responsibilities: - The Qualcomm Cloud team is developing hardware and software for Machine Learning solutions spanning the data center, edge, infrastructure, automotive market.
- We are seeking ambitious, bright, and innovative engineers with experience in machine learning framework development.
- Job activities span the whole product life cycle from early design to commercial deployment.
- The environment is fast-paced and requires cross-functional interaction daily so good communication planning and execution skills are a must We are seeking a highly skilled and motivated Language Model Engineer to join our team.
- The primary role of the engineer will be to train Large Language Models (LLMs) from scratch and fine-tune existing LLMs on various datasets using state-of-the-art techniques.
- 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.
Qualifications: - 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
- 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.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Software Engineering or related work experience.
- 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.
Qualifications
- 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
- 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.
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Software Engineering or related work experience.
- 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.
Responsibilities
- The Qualcomm Cloud team is developing hardware and software for Machine Learning solutions spanning the data center, edge, infrastructure, automotive market.
- We are seeking ambitious, bright, and innovative engineers with experience in machine learning framework development.
- Job activities span the whole product life cycle from early design to commercial deployment.
- The environment is fast-paced and requires cross-functional interaction daily so good communication planning and execution skills are a must We are seeking a highly skilled and motivated Language Model Engineer to join our team.
- The primary role of the engineer will be to train Large Language Models (LLMs) from scratch and fine-tune existing LLMs on various datasets using state-of-the-art techniques.
- 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.