Principal AI Performance Architect

$180,700 - $332,400/Yr

Qualcomm - Santa Clara, CA

Full-time - Senior

Santa Clara, CA

Computer and Electronic Product Manufacturing

About the position

The Principal AI Performance Architect at Qualcomm is responsible for driving functional, performance, and power enhancements in hardware to enable advanced AI training capabilities. This role involves understanding trends in machine learning network design, collaborating with customers to define hardware requirements, and architecting enhancements for efficient AI model training. The position requires expertise in hardware and software architecture, algorithm development, and system-on-chip design, contributing to the development of scalable AI inference and training systems.

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
• Analyze current accelerator and GPU architectures
• Architect enhancements required for efficient training of AI models
• Design and architecture of flexible computational blocks involving various data types and precision
• Optimize memory technology and subsystems for capacity, bandwidth, and compute requirements
• Develop scale-out and scale-up architectures including switches and NoCs
• Perform competitive analysis and co-design hardware with software/GenAI requirements
• Define performance models to validate architecture proposals
• Conduct pre-silicon performance predictions for various ML training workloads
• Analyze performance/area/power trade-offs for future HW and SW ML algorithms

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

Nice-to-haves

• 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 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 frameworks 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)

Benefits

• Competitive annual discretionary bonus program
• Opportunity for annual RSU grants
• Comprehensive benefits package designed to support success at work, at home, and at play