Senior Staff Engineer, Machine Learning Engineering ( On-device SW )

Qualcomm•San Diego, CA

6d•$194,400 - $291,600•Onsite

About The Position

Qualcomm AI Research is looking for talented AI software engineers to enable AI technologies on edge devices. This is your opportunity to join a high-caliber team of engineers, building best-in-class GenAI solutions with model optimization tools to deploy state-of-the-art models to edge devices with optimal power, memory, and computation use. In this role, you will be part of a multi-disciplinary team that has continually enabled first-of-its-kind and competitive generative AI models on Qualcomm AI accelerator engines, such as the world’s first on device text-to-image generation with stable diffusion, and text-to-text with large language model on device. You will collaborate in a cross-functional environment spanning hardware, software and systems and see your design in action on industry-leading chips embedded in the next generation of intelligent devices, such as smartphones, autonomous vehicles, robotics, and IOT devices.

Requirements

Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 6+ 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 5+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Science, Engineering, Information Systems, or related field and 4+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

Nice To Haves

5+ years of experience with Machine Learning frameworks and Deep Learning (Pytorch).
5+ years of experience with one or more programming language suitable for machine learning (e.g., Python, C, C++)
Strong software design, development, and debugging skills.
Knowledge of Android programming is plus.
Optimization of algebraic operations in algorithms for HW cores is a plus.
Knowledge in neural network model training is a big plus.
Knowledge in neural network model quantization is a big plus.
Experience with Qualcomm QNN SDK is a big plus.

Responsibilities

Development of end-to-end embedded AI software to train and finetune neural network models on Qualcomm leading edge hardware with optimal resources.
Design and enhance the implementation of ML/AI SW stack, kernels, and runtime software to improve performance and power efficiency.
Collaborating with our AI Processor Hardware team to implement high-quality solutions for new ML operators/layers that optimally utilize new capabilities in next-gen AI processors.
Development of debugging/profiling tools and user-friendly SDKs for customers to foster rapid deployment of their new use cases.