Role overview
As a leading technology innovator, Qualcomm pushes the boundaries of what's possible to enable next-generation experiences and drives digital transformation to help create a smarter, connected future for all. As a Qualcomm Machine Learning Engineer, you will create and implement machine learning techniques, frameworks, and tools that enable the efficient discovery and utilization of state-of-the-art machine learning solutions over a broad set of technology verticals or designs. Qualcomm Engineers collaborate with cross-functional teams to enhance the world of mobile, edge, auto, and IOT products through machine learning hardware and software.
What you'll work on
Applies Machine Learning knowledge to assist in extending training or runtime frameworks or model efficiency software tools with new features and optimizations.
Assists in the modeling, architecture, and development of machine learning hardware (co-designed with machine learning software) for inference or training solutions.
Assists in the development of optimized software to enable AI models deployed on hardware (e.g., machine learning kernels, compiler tools, or model efficiency tools, etc.) to allow specific hardware features; collaborates with team members for joint design and development.
Assists with the development and application of machine learning techniques into products and/or AI solutions to enable customers to do the same.
Develops, adapts, or prototypes machine learning algorithms, models, or frameworks in alignment with product roadmap.
What we're looking for
Master's degree in Computer Science, Engineering, Information Systems, or related field.
1+ year of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras).
1+ year of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media).
1 + year of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++)
1 + year of experience using statistics and probability (e.g., conditional probability, Bayes rule).
1 + year of experience working in a large matrixed organization.
6+ months of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware.