Senior AI Research Quantization Engineer

About the position

Qualcomm AI Research is looking for world-class algorithm engineers in general domain machine learning, especially deep learning, generative AI, LLM, LVM. Come join a high-caliber team of engineers building advanced machine learning technology, best-in-class solutions, and user friendly model optimization tools such as Qualcomm Innovation Center’s AI Model Efficiency Toolkit (https://github.com/quic/aimet) to enable state-of-the-art networks to run on devices with limited power, memory, and computation. Members of our team enjoy the opportunity to participate in cutting edge research while simultaneously contributing technology that will be deployed worldwide in our industry-leading devices. You will be part of a multi-disciplinary talented team working on on-device generative AI optimization. Collaborate in a cross-functional environment spanning hardware, software and systems. See your design in action on industry-leading chips embedded in the next generation of smartphones, autonomous vehicles, robotics, and IOT devices.\nThe R&D work responsibility for this position focuses on the following:\nAlgorithms research and development for efficient generative AI, LLM, LVM, Multi-modal, VLA\nEfficient inference algorithms, e.g. batching, KV caching, efficient attentions, long context, speculative decoding\nAdvanced quantization algorithms for complex generative models, e.g., gradient/non-gradient based optimization, equivalent/non-equivalent transformation, automatic mixed precision, hardware in loop\nModel compression, lossy or lossless, structural and neural search\nGenerative AI system prototyping\nApply solutions toward system innovations for model efficiency advancement on device as well as in the cloud\nPython, Pytorch programming.

Responsibilities

• Algorithms research and development for efficient generative AI, LLM, LVM, Multi-modal, VLA
• Efficient inference algorithms, e.g. batching, KV caching, efficient attentions, long context, speculative decoding
• Advanced quantization algorithms for complex generative models, e.g., gradient/non-gradient based optimization, equivalent/non-equivalent transformation, automatic mixed precision, hardware in loop
• Model compression, lossy or lossless, structural and neural search
• Generative AI system prototyping
• Apply solutions toward system innovations for model efficiency advancement on device as well as in the cloud
• Python, Pytorch programming.

Requirements

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

Nice-to-haves

• Master's degree in Computer Science, Engineering, Information Systems, or related field. PHD's degree is preferred.
• 2+ years of experience with Machine Learning algorithms or systems engineering or related work experience.