Performance Modeling Architect- Data Center GPU

THE ROLE:

As a Fellow/ Sr Fellow level Engineer you will spearhead performance analysis and modeling for AMD datacenter GPUs. You will lead efforts that enable massive model training at scale. Your expertise will lead teams to drive performance gains in both training and inference pipelines through innovative system design and optimization. You will champion adoption of cutting-edge techniques across the engineering organization. This role requires deep understanding of GPU microarchitecture, memory hierarchies, and their impact on large-scale ML workloads.

KEY RESPONSIBILITES:

• Lead performance modeling and optimization for multi-trillion parameter LLM training/inference including Dense, Mixture of Experts (MoE) with multiple modalities (text, vision, speech)

• Model/optimize novel parallelization strategies across tensor, pipeline, context, expert and data parallel dimensions

• Architect memory-efficient training systems utilizing techniques like structured pruning, quantization (MX formats), continuous batching/chunked prefill, speculative decoding

• Incorporate and extend SOTA models such as GPT-4, Reasoning models (Deepseek-R1), and multi-modal architectures

• Collaborate with internal and external stakeholders/ML researchers to disseminate results and iterate at rapid pace

PREFERRED EXPERIENCE:

• Deep experience optimizing large-scale ML systems and GPU architectures

• Strong track record of technical leadership in GPU performance and workload analysis including patents and recent publications, participation in industry forums and peer acknowledgement

• Deep expertise in CUDA programming, GPU memory hierarchies, and hardware-specific optimizations

• Proven track record architecting distributed training systems handling large scale systems

• Expert knowledge of transformer architectures, attention mechanisms, and model parallelism techniques

PREFERRED TECHNICAL COMPENTENCIES:

• PyTorch, CUDA, TensorRT, OpenAI Triton

• Distributed systems: Ray, Megatron-LM

• Performance analysis tools: NSight Compute, nvprof, PyTorch Profiler

• KV cache optimization, Flash Attention, Mixture of Experts

• High-speed networking: InfiniBand, RDMA, NVLink

ACADEMIC CREDENTIALS:

• Bachelors, MS/PhD in Computer Science/Engineering or equivalent industry experience

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