Senior AI Performance Architect

Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Software Engineering · Embedded Systems

In short

Qualcomm is seeking a Senior AI Performance Architect to drive functional, performance, and power enhancements in AI accelerators for state-of-the-art training capabilities. The role involves understanding ML network trends, working with customers on hardware requirements, analyzing architectures, and designing efficient computational blocks and memory subsystems for AI models.

Responsibilities

Understand trends in ML network design and their impact on SW and HW design.
Work with customers to determine hardware requirements for AI training systems.
Analyze current accelerator and GPU architectures.
Architect enhancements for efficient AI model training.
Design and architect flexible computational blocks supporting various datatypes and precisions.
Design and architect memory subsystems optimized for capacity and bandwidth.
Design and architect scale-out and scale-up architectures.
Optimize for power efficiency.
Perform competitive analysis.
Codesign HW with SW/GenAI (LLM) requirements.
Define performance models to prove architecture proposal effectiveness.
Perform pre-silicon prediction of performance for 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.

Desired Qualifications

Knowledge of computer architecture, digital circuits and hardware simulators.
Knowledge of communication protocols used in AI systems.
Knowledge of Network-on-Chip (NoC) designs.
Understanding of various memory technologies used in AI systems.
Experience in modeling hardware and workloads for 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 like DeepSpeed, FSDP.
Knowledge of front-end ML frameworks (i.e., TensorFlow, PyTorch).
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.
Highly competitive benefits package designed to support success at work, at home, and at play.