Qualcomm Jobs

Company:

Qualcomm China

Job Area:

Engineering Group, Engineering Group > Machine Learning Engineering

General Summary:

Qualcomm AI Research is looking for world-class algorithms engineers in general domain machine learning, especially deep learning, generative AI, LLM, LVM. Come join a high-caliber team of engineers building advanced machine learning technology, best-in-class solutions, and user friendly model optimization tools such as Qualcomm Innovation Center’s AI Model Efficiency Toolkit ( https://github.com/quic/aimet ) to enable state-of-the-art networks to run on devices with limited power, memory, and computation. Members of our team enjoy the opportunity to participate in cutting edge research while simultaneously contributing technology that will be deployed worldwide in our industry-leading devices. You will be part of a multi-disciplinary talented team working on on device generative AI optimization. Collaborate in a cross-functional environment spanning hardware, software and systems. See your design in action on industry-leading chips embedded in the next generation of smartphones, autonomous vehicles, robotics, and IOT devices.

The R&D work responsibility for this position focuses on the following

• Algorithms research and development in the area of GenerativeAI, LVM, LLM, Multi-modality

• Efficient inference algorithms research and development, e.g. batching, KV caching, efficient attentions, long context, speculative decoding

• Advanced quantization algorithms research and development for complex generative models, e.g. GPTQ, Aumatic mixed precision, hardware in loop,

• Model compression, lossy or lossless, structural and neural search.

• Optimization based learning and learning based optimization.

• Generative AI Systems prototyping

• Apply solutions toward systems innovations for model efficiency advancement on device as well as in the cloud.

• Python, Pytorch programmer

Minimum Qualifications:

• 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.

OR

Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.

OR

PhD in Computer Science, Engineering, Information Systems, or related field.

Preferred Qualifications:

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

• 2+ years of experience with Machine Learning algorithms or systems engineering or related work experience.

All Qualcomm employees are expected to actively support diversity on their teams, and in the Company.

Although this role has some expected minor physical activity, this should not deter otherwise qualified applicants from applying. If you are an individual with a physical or mental disability and need an accommodation during the application/hiring process, please call Qualcomm’s toll-free number found here (https://qualcomm.service-now.com/hrpublic?id=hr_public_article_view&sysparm_article=KB0039028) for assistance. Qualcomm will provide reasonable accommodations, upon request, to support individuals with disabilities as part of our ongoing efforts to create an accessible workplace.

Qualcomm is an equal opportunity employer and supports workforce diversity.

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