Senior AI Performance Architect
Technology, Data & Digital · Data, AI & Analytics · Machine Learning · Data Engineering · Software Engineering
In short
Qualcomm is looking for a Senior AI Performance Architect to design and enhance AI training systems, focusing on hardware accelerators, memory, and scale-out architectures. This role involves understanding ML trends, customer requirements, and performing detailed performance/power trade-offs for future AI models.
Responsibilities
- Understand ML network design trends and their impact on SW/HW design.
- Work with customers to define 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 with various datatypes and precisions (e.g., floating point, 1/2/4/8/16/32-bit).
- Design and architect memory subsystems optimized for capacity, bandwidth, and compute-in-memory/near-memory.
- Design and architect scale-out and scale-up architectures, including switches, NoCs, and communication collectives.
- Optimize architectures for power efficiency.
- Perform competitive analysis.
- Codesign HW with SW/GenAI (LLM) requirements.
- Define performance models to validate architecture proposals.
- Perform pre-silicon performance prediction for ML training workloads.
- Analyze performance/area/power trade-offs for future ML algorithms and SOC components.
Requirements
- Master's degree in Computer Science, Engineering, Information Systems, or related field.
- 3+ years of Hardware Engineering experience defining architecture of GPUs or accelerators for AI model training.
- 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 in System-on-Chip (SoC) 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 (TensorFlow, PyTorch) for ML model training.
- 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 various ML model classes (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.
- Flexible Location (Remote)
Skills
Machine LearningHardware EngineeringGPUsLLMComputer ArchitectureDigital CircuitsHardware SimulatorsNetwork-on-Chip (NoC)System-on-Chip (SoC)DeepSpeedFSDPTensorFlowPyTorchC++PythonCNNRNN
#AI#Machine Learning#Architecture#Hardware#Performance#GPU#Accelerator#Training#Deep Learning#Data Center#End Devices#Edge#Cloud
About Qualcomm
Enabling a world where everyone and everything can be intelligently connected.
Enabling a world where everyone and everything can be intelligently connected.\n\n
Company
QualcommJob Posted
4 days ago
Employment Type
Full Time
Work mode
Remote
Salary
US$126,700–190,100 / year
Experience Level
Senior
Locations
Raleigh, United States of America
Qualification
Bachelor, Master, Doctoral
Applicants
Be an early applicant
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