Qualcomm
Principle AI Performance Modeling Architect
Qualcomm, San Diego, California, United States, 92189
Overview
Company Qualcomm Technologies, Inc. Job Area Engineering Group, Engineering Group > Machine Learning Engineering General Summary
Today, more intelligence is moving to end devices, and mobile is becoming a pervasive AI platform. At the same time, data centers are expanding AI capability through widespread deployment of ML accelerators. Qualcomm envisions making AI ubiquitous - expanding beyond mobile and powering other end devices, data centers, vehicles, and things. We are inventing, developing, and commercializing power-efficient on-device AI, edge cloud AI, data center and 5G to make this a reality. The Cloud AI Architecture team is comprised of experts that span the full gamut from performance modeling, software architecture, algorithm development, kernel optimization, down to hardware accelerator block architecture and SOC design. We are looking for an AI Accelerator Architect who would lead a team developing performance models that accurately estimate workload performance and power characteristics given known AI architectures. This team also executes developed models against various workloads. These models will be used to estimate performance for various internal teams and external customers. Responsibilities
Define and develop performance models designed to estimate performance and power usage of AI workloads Lead a team of performance architects, providing guidance for performance studies and model enhancements Execute models against requested workloads and configurations Understand trends in ML network design through customer engagements and latest academic research and determine how this will affect both SW and HW design Work with customers to understand workloads and performance requirements Analysis of current accelerator and GPU architectures Suggest hardware enhancements required for efficient execution of AI workloads Pre-Silicon prediction of performance for various ML training workloads Perform analysis of performance/area/power trade-offs for future HW and SW ML algorithms including impact of SOC components (memory and bus impacts) Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 8+ 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 7+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. Minimum Requirements
Master\'s degree or equivalent in Engineering, or related field. 8+ years Hardware Engineering, Systems Engineering, or related work experience. 3+ years experience performance modeling of AI accelerators or similar systems Team leadership experience In-depth knowledge of nVidia/AMD GPGPU capabilities and architectures Knowledge of LLM architectures and their HW requirements Preferred Skills And Experience
Knowledge of computer architecture, digital circuits Knowledge of modeling of communication systems Knowledge of communication protocols used in AI systems Understanding of Network-on-Chip (NoC) designs used in System-on-Chip (SoC) designs Understanding of various memory technologies used in AI systems High-level architectural-level hardware modeling experience preferred Knowledge of AI Inference and Training systems such as NVIDIA DGX and NVL72 Strong communication skills (written and verbal) Detail-oriented with strong problem-solving, analytical and debugging skills Good leadership abilities with attention given to tracking tasks and ensuring timely completion of those tasks Proficient in Excel including VBA scripting Demonstrated ability to learn, think and adapt in a fast-changing environment Ability to code in C++ and Python Understanding of elemental operations performed in ML workloads 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 disability-accomodations@qualcomm.com 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). 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. Pay Range And Other Compensation & Benefits $180,700.00 - $332,400.00 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. Salary is only one component of total compensation at Qualcomm. We offer a competitive annual discretionary bonus program and opportunity for annual RSU grants. Our benefits package supports success at work, at home, and at play. Your recruiter can discuss details about Qualcomm\'s offerings. If you would like more information about this role, please contact Qualcomm Careers.
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Company Qualcomm Technologies, Inc. Job Area Engineering Group, Engineering Group > Machine Learning Engineering General Summary
Today, more intelligence is moving to end devices, and mobile is becoming a pervasive AI platform. At the same time, data centers are expanding AI capability through widespread deployment of ML accelerators. Qualcomm envisions making AI ubiquitous - expanding beyond mobile and powering other end devices, data centers, vehicles, and things. We are inventing, developing, and commercializing power-efficient on-device AI, edge cloud AI, data center and 5G to make this a reality. The Cloud AI Architecture team is comprised of experts that span the full gamut from performance modeling, software architecture, algorithm development, kernel optimization, down to hardware accelerator block architecture and SOC design. We are looking for an AI Accelerator Architect who would lead a team developing performance models that accurately estimate workload performance and power characteristics given known AI architectures. This team also executes developed models against various workloads. These models will be used to estimate performance for various internal teams and external customers. Responsibilities
Define and develop performance models designed to estimate performance and power usage of AI workloads Lead a team of performance architects, providing guidance for performance studies and model enhancements Execute models against requested workloads and configurations Understand trends in ML network design through customer engagements and latest academic research and determine how this will affect both SW and HW design Work with customers to understand workloads and performance requirements Analysis of current accelerator and GPU architectures Suggest hardware enhancements required for efficient execution of AI workloads Pre-Silicon prediction of performance for various ML training workloads Perform analysis of performance/area/power trade-offs for future HW and SW ML algorithms including impact of SOC components (memory and bus impacts) Minimum Qualifications
Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 8+ 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 7+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field and 6+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. Minimum Requirements
Master\'s degree or equivalent in Engineering, or related field. 8+ years Hardware Engineering, Systems Engineering, or related work experience. 3+ years experience performance modeling of AI accelerators or similar systems Team leadership experience In-depth knowledge of nVidia/AMD GPGPU capabilities and architectures Knowledge of LLM architectures and their HW requirements Preferred Skills And Experience
Knowledge of computer architecture, digital circuits Knowledge of modeling of communication systems Knowledge of communication protocols used in AI systems Understanding of Network-on-Chip (NoC) designs used in System-on-Chip (SoC) designs Understanding of various memory technologies used in AI systems High-level architectural-level hardware modeling experience preferred Knowledge of AI Inference and Training systems such as NVIDIA DGX and NVL72 Strong communication skills (written and verbal) Detail-oriented with strong problem-solving, analytical and debugging skills Good leadership abilities with attention given to tracking tasks and ensuring timely completion of those tasks Proficient in Excel including VBA scripting Demonstrated ability to learn, think and adapt in a fast-changing environment Ability to code in C++ and Python Understanding of elemental operations performed in ML workloads 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 disability-accomodations@qualcomm.com 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). 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. Pay Range And Other Compensation & Benefits $180,700.00 - $332,400.00 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. Salary is only one component of total compensation at Qualcomm. We offer a competitive annual discretionary bonus program and opportunity for annual RSU grants. Our benefits package supports success at work, at home, and at play. Your recruiter can discuss details about Qualcomm\'s offerings. If you would like more information about this role, please contact Qualcomm Careers.
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