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Machine Learning Engineering

1 month ago

San Diego, United States Qualcomm Full time

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.

Preferred Qualifications:

•    Master's degree in Computer Science, Engineering, Information Systems, or related field.

•    5+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras).
•    5+ years of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware.

•    5+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media).

•    5+ years of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++)

•    5+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule).

•    3+ years experience working in a large matrixed organization.

•    2+ years of work experience in a role requiring interaction with senior leadership (e.g., Director and above).

•    1+ year in a technical leadership role with or without direct reports (only applies to positions with direct reports).

•    Developed 1+ novel Machine Learning architecture(s).

Principal Duties and Responsibilities:

•    Leverages advanced Machine Learning knowledge to extend training or runtime frameworks or model efficiency software tools with new features and optimizations.

•    Models, architects, and develops highly advanced machine learning hardware (co-designed with machine learning software) for inference or training solutions.

•    Develops critical 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 hardware teams for joint design and development. 

•    Leads 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 novel machine learning solutions aligned with and motivated by proposals or roadmaps for complex products and system-level features. 

•    Oversees and provides technical expertise to others conducting experiments to train and evaluate machine learning solutions.

Level of Responsibility:
•    Works independently with minimal supervision.

•    Provides supervision/guidance to other team members.
•    Decision-making is significant in nature and affects work beyond immediate work group.
•    Requires verbal and written communication skills to convey complex information. May require negotiation, influence, tact, etc. 

•    Has a moderate amount of influence over key organizational decisions (e.g., is consulted by senior leadership to make key decisions).

• Tasks do not have defined steps; planning, problem-solving, and prioritization must occur to complete the tasks effectively.