Senior AI Training & Edge Accelerator Architect

Qualcomm Incorporated; San Diego (CA); On-site; USD 127,000 - 190,000; Full time

Job summary

Qualcomm Technologies, Inc. is seeking a hardware-focused ML engineer to drive architecture for AI training on diverse accelerators and GPUs.

The role focuses on designing flexible compute blocks, diverse datatypes and precisions, and scale-out/in architectures, with a strong emphasis on power efficiency and performance modeling. The candidate will engage with customers, analyze current hardware, and collaborate with HW/SW teams to advance GenAI readiness through pre-silicon modeling and NoC/SoC

Qualifications

• Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
• 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.
• PhD in Computer Science, Engineering, Information Systems, or related field.

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 various datatypes (floating point to 1) and precision (32/16/8/4/2/1), GEMM/GEMV performance
• Memory technology and subsystems optimized for a range of requirements
• 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 SOC components (memory and bus impacts)

Skills

C++

Python

GPU Architectures

LLM Architectures

DeepSpeed

TensorFlow

PyTorch

NoC/SoC

Hardware Modeling

Education

Bachelor's degree in Computer Science or Engineering

Master's degree in Computer Science or Engineering

PhD in Computer Science or Engineering

Job description

Qualcomm Technologies, Inc. is seeking a hardware-focused ML engineer to drive architecture for AI training on diverse accelerators and GPUs.

The role focuses on designing flexible compute blocks, diverse datatypes and precisions, and scale-out/in architectures, with a strong emphasis on power efficiency and performance modeling. The candidate will engage with customers, analyze current hardware, and collaborate with HW/SW teams to advance GenAI readiness through pre-silicon modeling and NoC/SoC