Machine Learning Research Engineer, Senior

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Job Area:

General Summary:

Qualcomm AI Research is looking for world-class researchers in deep learning to join a high-caliber team of engineers inventing machine-learning technology to provide best-in-class solutions while running with the most efficient use of power, memory, and computation.

Members of our team are part of a multi-disciplinary core research group within Qualcomm which spans software, hardware, and systems. Our members contribute technology deployed worldwide by partnering with our business teams across mobile, compute, automotive, cloud, and IOT. We also perform and publish state-of-the-art research on a wide range of topics in machine-learning, ranging from general theory to techniques that enable deployment on resource-constrained devices. Our research team has demonstrated first-in-the-world research and proof-of-concepts in areas such model efficiency, neural video codecs, generative ai, video semantic segmentation, federated learning, and wireless RF sensing, to name a few. We work to then convert our research and industry state-of-the-art methods. Please see our AI focus page at https://qualcomm.com/ai for more details.

Role responsibility can include both applied and fundamental research in the field of machine learning with development focus in one or many of the following areas:

• Drives systems innovations for model efficiency advancement on device as well as in the cloud. This includes model compression, model sparsity, quantization optimization methods, plus unified optimization of models and their implementation on-target to balance memory, speed, and power.
• Conducts fundamental machine learning research to create new models or new training methods in various technology areas, e.g., large language models, deep generative models (transformers, state-space networks, diffusion networks, VAEs, etc.), reasoning models, agentic frameworks, and reinforcement learning techniques.
• Performs advanced platform research to enable new machine learning compute paradigms, e.g., compute in memory, on-device learning/training, edge-cloud distributed/federated learning, quantum machine learning, causal and language-based reasoning.
• Creates new machine learning models for advanced use cases that achieve state-of-the-art performance and beyond. The use cases can broadly include computer vision, audio, speech, NLP, image, video, power management, wireless, graphics, and chip design.
• Design, develop & test software for machine learning frameworks that optimize models to run efficiently on edge devices. Candidate is expected to have strong interest and deep passion on making leading-edge deep learning algorithms work on mobile/embedded platforms for the benefit of end users.
• Research, design, develop, enhance, and implement different components of machine learning compiler for HW Accelerators.
• Design, implement and train DL/RL algorithms in high-level languages/frameworks.

Minimum Qualifications:

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.

Qualifications:

· PhD in AI, Computer Science, Engineering, Information Systems, or related field, or MS with 4+ years of machine learning research, or related work experience.

· Strong background in computer science and deep learning basics

· Strong programming skills in Python and PyTorch. Proficiency in designing and implementing complex training and evaluation pipelines.

· Exceptional analytical, development, debugging and problem solving skills

Preferred qualifications:

· Experience with LLM, LVM, LMM models, and other neural network architectures.

· Excellent interpersonal, written, and oral communications skills

· Experience with machine learning accelerators, optimizing algorithms for hardware acceleration cores, working with heterogeneous or parallel computing systems.

· Design and develop generalized AI solutions, including retrieval and agentic systems.

· Experience in LLM reasoning or inference acceleration research

· Experience of on-device AI model production or exposure to mobile / edge device environments

· First author publications experience at major AI conferences, e.g., NeurIPS, ICML, and ICLR

Qualcomm is an equal opportunity employer. If you are an individual with a disability and need an accommodation during the application/hiring process, rest assured that Qualcomm is committed to providing an accessible process. You may e-mail [email protected] or call Qualcomm's toll-free number found here. Upon request, Qualcomm will provide reasonable accommodations to support individuals with disabilities to be able participate in the hiring process. Qualcomm is also committed to making our workplace accessible for individuals with disabilities. (Keep in mind that this email address is used to provide reasonable accommodations for individuals with disabilities. We will not respond here to requests for updates on applications or resume inquiries).

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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.

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Pay rangeand Other Compensation & Benefits :

The above pay scale reflects the broad, minimum to maximum, pay scale for this job code for the location for which it has been posted. Even more importantly, please note that salary is only one component of total compensation at Qualcomm. We also offer a competitive annual discretionary bonus program and opportunity for annual RSU grants (employees on sales-incentive plans are not eligible for our annual bonus). In addition, our highly competitive benefits package is designed to support your success at work, at home, and at play. Your recruiter will be happy to discuss all that Qualcomm has to offer - and you can review more details about our US benefits at this link.

If you would like more information about this role, please contact Qualcomm Careers.