Qualcomm Jobs

Company:

Qualcomm Semiconductor Limited

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

Qualcomm Overview:

Qualcomm is a company of inventors that unlocked 5G ushering in an age of rapid acceleration in connectivity and new possibilities that will transform industries, create jobs, and enrich lives. But this is just the beginning. It takes inventive minds with diverse skills, backgrounds, and cultures to transform 5Gs potential into world-changing technologies and products. This is the Invention Age - and this is where you come in.

General Summary:

Job Overview:

Qualcomm stands as a beacon of innovation, pioneering the forefront of on-device AI and unlocking the transformative potential of 5G. Our breakthroughs herald an era of unparalleled connectivity, sparking possibilities that will reshape industries, generate employment, and enrich lives. At the heart of this revolutionary era, Qualcomm's commitment to on-device AI leadership stands unmatched. Shaping 5G's potential into world-altering technologies demands inventive minds, diverse skills, and cultural backgrounds—a defining epoch known as the Age of Invention. Are you poised to immerse yourself in the forefront of technological evolution, comparable in significance to the advent of the Internet? Machine learning is poised to revolutionize EVERYTHING. In Taiwan, our AI Software Engineering team, collaborating extensively with global AI R&D teams, is at the helm of Qualcomm's pioneering advancements. Join us in leveraging your skills to expand Qualcomm's trailblazing AI portfolio (Qualcomm AI Stack), with a distinct emphasis on a software platform tailored for AI applications in Mobile, Automotive, IoT, and HPC devices, establishing our leadership in the AI PC domain. We integrate cutting-edge hardware with high-performance software, empowering rapid execution of deep neural networks on our unparalleled range of Snapdragon processors, setting the benchmark for minimal power consumption. Your role here is pivotal, enabling advanced machine learning scenarios atop Qualcomm's hardware and software infrastructure. You'll contribute to system software, tool development, maintenance, and evolution for various ML computing SDKs tailored for Qualcomm processors, both on Windows OS and Android OS. Amplifying the SDK's capabilities, collaborate with neural network frameworks like PyTorch and TensorFlow. Extend our neural net engine to support the latest DNNs emerging from the research community and optimize for the next-gen hardware acceleration cores. Additionally, validate engine performance and accuracy through meticulous analysis and comprehensive test coverage. Thrive in software development, wielding exceptional analytical, development, and debugging skills. Partner with industry leaders to usher in the next generation of machine learning technology, solidifying Qualcomm's pioneering stance in the AI PC domain.

The Role

Software development engineer working on Qualcomm AI Stack, an AI model inferencing software scalable to Qualcomm platforms.

The Person

Developing in the core libraries for AI workload on NPU. Having strong sense and knowledge of fine-grained AI operation performance analysis/optimization, and solid analytical and problem-solving skills.

Key Responsibilities

· Design, implement, optimize, profiling, analysis AI computing operations running on Qualcomm NPU.

· Deliver high-quality code working with open-source software communities.

· Work with key technical specialists across Qualcomm, our partners and/or customers to improve libraries for commercial use cases and/or industrial benchmarks.

Qualifications(for new grad)

· Master's degree in Computer Science, Electrical Engineering, or related field.

· Proficiency in programming languages such as C, C++, or Python.

· Strong knowledge of Object-Oriented Programming, data structures, algorithms and operating system.

· Self-motivated and capable of working independently with minimal oversight.

Ability to communicate technical concepts effectively and work collaboratively within cross-functional teams.

Minimum Qualifications(for experienced engineers)

· Bachelor's degree in Computer Science, Electrical Engineering, or a related field.

· Proficiency in languages like C++, CUDA, or OpenCL for GPU programming.

· Understanding of GPU/DSP/NPU architectures and parallel programming concepts.

· Familiarity with embedded systems and constraints related to edge devices.

· Experience in low-level programming for efficient hardware utilization.

· Knowledge of optimizing algorithms and libraries for GPU/DSP/NPU execution.

· Ability to analyze and optimize performance bottlenecks in GPU/NPU code.

· Proficiency with version control systems like Git and software development tools like Gerrit and JIRA.

Preferred Qualifications(for experienced engineers)

· Master's or Ph.D. in Computer Science, Electrical Engineering, or a related field.

· Familiarity with frameworks such as TensorFlow, PyTorch for DSP/GPU/NPU integration.

· Understanding of mainstream ML runtime frameworks formats and their runtime environments.

· Experience in optimizing code specifically for different DSP/GPU/NPU architectures.

· Understanding of machine learning and deep learning concepts for efficient utilization of hardware.

· Proven experience in fine-tuning performance-critical applications on edge devices.

· Knowledge of hardware-software co-design principles for edge devices.

· Proficiency in using tools for debugging and profiling GPU/NPU code.

· Strong communication skills to collaborate with hardware engineers and communicate complex technical concepts effectively.

Expertise in at least one of the following areas

· DSP software development and algorithm implementation: Proficiency in designing and implementing algorithms for Digital Signal Processing (DSP) in software.

· OpenCL programming and debugging: Experience in programming and debugging using OpenCL, particularly for parallel computing applications.

· Performance profiling and optimization for parallel computing: Strong understanding and practical experience in profiling and optimizing performance for parallel computing tasks.

· Image processing, computer vision, and object/face detection specifically: Expertise in image processing, computer vision techniques, and specialized knowledge in object/face detection methodologies.

· Understanding compiler technology: Familiarity with ML compilers such as TVM/Glow/XLA, as well as experience in LLVM/GCC backend development, optimization analysis, and implementation is highly advantageous.

· AI experience: Knowledge of Deep Learning/CNN fundamentals, proficiency in training neural networks to solve real-world problems, and familiarity with frameworks like TensorFlow/PyTorch are considered strong assets for this role.

Enthusiasm in machine learning technology. Hand-on experience in design/implementation of deep learning networks via modern frameworks, including TensorFlow, Pytorch, etc. Ability to quickly learn new technologies and work on resolving customer reported technical problems during mobile or wireless communication product development cycles. Excellent analytical, problem solving and Communication skills and willingness to work with customers

Applicants : If you need an accommodation, during the application/hiring process, you may request an accommodation by sending email to accommodationsupport

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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If you would like more information about this role, please contact Qualcomm Careers (http://www.qualcomm.com/contact/corporate) .

EEO Employer: Qualcomm is an equal opportunity employer; all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or any other protected classification