Principal AI Performance Architect Full-time

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.

We are looking for AI Accelerator Architecture Engineers to drive functional, performance and power enhancements into the HW to enable state of the art training capabilities. AI inference and training systems must scale to a large number of accelerators, servers and racks. Our devices must be designed to scale to handle the largest of today’s models.

The AI Architecture team is comprised of experts that span the full gamut from software architecture, algorithm development, kernel optimization, down to hardware accelerator block architecture and SOC design. The ideal candidate will augment the team by contributing to one or many of these areas.

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.

Responsibilities:

• 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 determine hardware requirements for AI training systems

• Analysis of current accelerator and GPU architectures
• Architect enhancements required for efficient training of AI models
• Design and architecture of:
• Flexible Computational Blocks
  • Involving a variety of datatypes : floating point, fixed point, microscaling
  • Involving a variety of precision : 32/16/8/4/2/1
  • Capable of optimally performing dense and sparse GEMM, GEMV

• Memory Technology and subystems that are optimized for a range of requirements
  • Capacity
  • Bandwidth
  • Compute in Memory, Compute near memory

• Scale-Out and Scale-Up Architectures
  • Switches, NoCs, Codesign with Communication Collectives

• Optimized for Power
• Ability to perform Competitive Analysis
• Codesign HW with SW/GenAI (LLM) requirements
• Define performance models to prove effectiveness of architecture proposals
• 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)

Requirements:

• Master’s degree in Computer Science, Engineering, Information Systems, or related field
• 3+ years Hardware Engineering experience defining architecture of GPUs or accelerators used for training of AI models
• In-depth knowledge of nVidia/AMD GPU capabilities and architectures
• Knowledge of LLM architectures and their HW requirements

Preferred Skills and Experience:

• Knowledge of computer architecture, digital circuits and hardware simulators
• Knowledge of communication protocols used in AI systems
• Knowledge of Network-on-Chip (NoC) designs used in System-on-Chip (SoC) designs
• Understanding of various memory technologies used in AI systems
• Experience in modeling hardware and workloads in order to extract performance and power estimates
• High-level hardware modeling experience preferred
• Knowledge of AI Training systems such as NVIDIA DGX and NVL72
• Experience training and finetuning LLMs using distributed training framework such as DeepSpeed, FSDP
• Knowledge of front-end ML frameworks (i.e.,TensorFlow, PyTorch) used for training of ML models
• Strong communication skills (written and verbal)
• Detail-oriented with strong problem-solving, analytical and debugging skills
• Demonstrated ability to learn, think and adapt in a fast-changing environment
• Ability to code in C++ and Python
• Knowledge of a variety of classes of ML models (i.e. CNN, RNN, etc)

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).

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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. 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.