Qualcomm ML-Embedded Software Engineer in Toronto, Ontario
Qualcomm Canada ULC
Engineering Group, Engineering Group > Machine Learning Engineering
Creates advanced machine learning techniques that enable a broad set of technology verticals or designs and extends training or runtime frameworks or model efficiency software tools with new features and optimizations. Models, architects, and develops advanced machine learning hardware (co-designed with machine learning software) for inference or training solutions. These enable the discovery and improvement of state-of-the-art machine learning solutions that has general applicability at functional platform level towards audio, camera, graphics, video, sensors, wireless, and other functionality over various operating systems running on ARM processors and other embedded hardware like DSP/NSP processors, GPU processors that are embedded into mobile, edge, auto, and IOT products, or on data center based systems such as GPUs or dedicated AI hardware. Develops optimized software to enable AI models efficiently deployed on hardware, such as machine learning kernels, compiler tools, or model efficiency tools, to make sure of specific hardware features, and/or working closely with hardware teams for joint design and development. Works with and/or optimize machine learning software frameworks like TensorFlow or PyTorch to efficiently run machine learning algorithms on hardware.
• 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+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
PhD in Computer Science, Engineering, Information Systems, or related field.
Preferred Qualifications: • Master's degree in Computer Science, Engineering, Information Systems, or related field. • 2+ years of experience with Machine Learning frameworks (e.g., Tensor Flow, Caffe, Caffe 2, Pytorch, Keras). • 2+ years of experience in embedded system development and optimization with application to a specific problem domain in ML (e.g., NLP, multi-media). • 2+ years of experience with one or more programming language suitable for machine learning (e.g., Python, R, C, C++) • 2+ years of experience using statistics and probability (e.g., conditional probability, Bayes rule). • 1+ years of experience with low level interactions between operating systems (e.g., Linux, Android, QNX) and Hardware. • 2+ years experience working in a large matrixed organization. • 1+ years of work experience in a role requiring interaction with senior leadership (e.g., Director and above).
Principal Duties and Responsibilities: • Participates in and shares own perspective within domain of machine learning subject matter expertise in design or project reviews, and project meetings. • Takes responsibility for small projects or owns part of a larger project and completes tasks in a timely manner according to project requirements; seeks assistance when needed to solve problems and helps other team members; collaborates with cross-functional peers on tasks when needed. • Completes complex tasks and solves issues related to the engineering and management of machine learning data with minimal guidance from more experienced engineers. • Develops, adapts, or prototypes complex machine learning algorithms, models, or frameworks aligned with and motivated by product proposals or roadmaps with minimal guidance from more experienced engineers. • Conducts complex experiments to train and evaluate machine learning models and/or software independently. • Creates and executes complex methods to optimize new or existing machine learning algorithms, models, kernels, and execution frameworks; suggests possible solutions to issues and documents lessons learned. • Assists with the integration of machine learning algorithms into a platform or product for production; helps resolve issues during implementation. • Seeks essential knowledge of machine learning industry trends, competitors' products, and advances within area of expertise from publicly available information and research; shares this information with others on the team.
Level of Responsibility: • Working under some supervision. • Providing some supervision/guidance to others. • Taking responsibility for own work and making decisions with limited impact; impact of decisions is readily apparent; errors made typically only impact timeline (i.e., require additional time to correct). • Using verbal and written communication skills to convey information that may be somewhat complex to others who may have limited knowledge of the subject in question. May require basic negotiation and influence, cooperation, tact, and diplomacy, etc. • Having a moderate amount of influence over key organizational decisions (e.g., is consulted by senior leadership to provide input on key decisions). • Completing most tasks with multiple steps which can be performed in various orders; some planning and prioritization must occur to complete the tasks effectively; mistakes may result in some rework. • Exercising creativity to draft original documents, imagery, or work products within established guidelines. • Using deductive and inductive problem solving; multiple approaches may be taken/necessary to solve the problem; often information is missing or conflicting; advanced data analysis and interpretation skills are required. • May be solicited during strategic planning period.
The responsibilities of this role do not include: • Financial accountability (e.g., does not involve budgeting responsibility).
Qualcomm is committed to hiring and supporting individuals with disabilities. Although this role has some expected physical activity, an inability to perform one or more of the listed physical requirements should not deter otherwise qualified applicants from applying. We will work with you throughout the application and onboarding process to provide reasonable accommodations. Examples of expected physical activity include: frequently transporting between offices, buildings, and campuses up to ½ mile; frequently transporting and installing equipment up to 5 lbs.; performing tasks at various heights (e.g., standing or sitting); monitoring and utilizing computers and test equipment for more than 6 hours a day; and continuous communication which includes the comprehension of information with colleagues, customers, and vendors both in person and remotely.
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