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Company Overview
A leading global technology company is driving innovation in AI across mobile, edge, and data center platforms. The organization focuses on power-efficient AI solutions for devices, servers, and large-scale AI training systems, combining expertise in software, hardware accelerators, and system-on-chip (SoC) design to deliver cutting-edge AI performance.
Summary
The Senior AI Performance Architect will contribute to AI accelerator architecture, optimizing performance, scalability, and power efficiency for AI training and inference systems. This role requires deep expertise in hardware design, AI workloads, and system-level co-design to meet the demands of large-scale AI models and applications.
Responsibilities
Analyze trends in ML network design through customer engagements and research to guide software and hardware architecture decisions
Collaborate with customers to define hardware requirements for AI training systems
Evaluate current GPU and accelerator architectures and propose enhancements for efficient AI model training
Architect and design flexible computational blocks supporting various datatypes and precision levels (32/16/8/4/2/1)
Design memory technologies and subsystems optimized for capacity, bandwidth, and compute-in/near-memory operations
Develop scale-out and scale-up architectures, including switches, NoCs, and codesign with communication collectives
Optimize architectures for power efficiency
Perform competitive analysis and HW/SW co-design to support GenAI and LLM requirements
Define performance models and conduct pre-silicon performance predictions for ML training workloads
Analyze performance, area, and power trade-offs for future hardware and software ML algorithms, including SoC components
Requirements
Master’s degree in Computer Science, Engineering, Information Systems, or related field
3+ years of hardware engineering experience defining architectures for GPUs or AI accelerators used in model training
Deep knowledge of NVIDIA/AMD GPU capabilities and architectures
Understanding of LLM architectures and their hardware requirements
Preferred Skills and Experience
Strong background in computer architecture, digital circuits, and hardware simulation
Knowledge of communication protocols and NoC designs used in AI systems
Familiarity with memory technologies for AI workloads
Experience modeling hardware and workloads to extract performance and power estimates
High-level hardware modeling experience
Knowledge of AI training systems such as NVIDIA DGX and NVL72
Experience with distributed training frameworks (DeepSpeed, FSDP) for LLMs
Proficiency in front-end ML frameworks (TensorFlow, PyTorch) for training models
Strong coding skills in C++ and Python
Excellent communication, analytical, problem-solving, and debugging skills
Ability to adapt and learn in fast-changing environments
Understanding of various ML model classes (CNN, RNN, etc.)
Disclaimer
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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality. 2. No Client Relationship: This company is not a client of SpringCube unless stated. 3. To Apply: Click the “Apply” button to be redirected to the hiring company’s application page for this job. 4. No Liability: SpringCube is not liable for inaccuracies.
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