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# Senior AI Performance Architect

- **Company:** Qualcomm
- **Location:** Santa Clara, CA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $126,700.0 - $190,100.0
- **Contract:** Permanent contract
- **Skills:** Systems Engineering, C++ (Programming Language), Communications Protocols, Information Systems, Computer Engineering, Software Debugging, Digital Electronics, Python (Programming Language), Machine Learning, Network Planning and Design, Performance Tuning, Tensorflow, Software Engineering, System on a Chip, Graphics Processing Unit (GPU), Pytorch, DeepSpeed, Large Language Models, Information Technology
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=3a6b98070fb700a7

## About the Role

* 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), * 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.
OR
PhD in Computer Science, Engineering, Information Systems, or related field.

## Description

* 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)