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)
Minimum 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.
- OR
- 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.
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Pay range and Other Compensation & Benefits
$126,700.00 - $190,100.00
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.
