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Job Area:
Interns Group, Interns Group > Interim Intern
you will be involved and participate in building best-in-class solutions and tools needed to enable state-of-the-art technologies for next generation mobile/embedded machine learning platforms for smart phones, computing, robotics and IOT devices. In this role, you will work in a dynamic environment, be part of a multi-disciplinary team, collaborate with internal teams, work with popular neural network frameworks, and understand the architecture of Qualcomm’s SOC and ML HW accelerators. You will create applications using machine learning tools and frameworks that enable making models smaller and run efficiently on all edge devices.
Minimum Qualifications
Currently enrolled in a bachelor’s degree program in computer engineering, computer science, electrical engineering, or a related field.
1+ years’ experience with programming language such as C, C++, Python.
Familiar with embedded systems - exposure to one or more of Linux, Yocto, Ubuntu based systems.
Proficiency in English.
Preferred Qualifications
Strong communication and interpersonal skills, high motivation, the ability to learn quickly and must be a self-starter.
Proficiency in deep learning and machine learning frameworks like TensorFlow, TFLite, PyTorch and Onnx Runtime.
Theoretical knowledge of machine learning, deep learning, model compression, quantization, and optimization.
Have knowledge in Qualcomm SNPE/QNN SDK is a plus.
Have knowledge in on-device AI models deployment is a plus.
Experience with reinforcement learning, neural architecture search, kernel optimization, and Bayesian optimization.
Skills in neural network programming, video/image processing, and application development.
Proficiency in machine learning data pipelines, data management, and both backend and frontend applications.
Education
Qualcomm expects its employees to abide by all applicable policies and procedures, including but not limited to security and other requirements regarding protection of Company confidential information and other confidential and/or proprietary information, to the extent those requirements are permissible under applicable law.
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