Fellow, Performance Modeling Architect- Data Center GPU Job Listing at AMD in Austin, TX (Job ID 65005-en-us)

Description

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Fellow 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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Benefits offered are described:  AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants' needs under the respective laws throughout all stages of the recruitment and selection process.