Note: The job is a remote job and is open to candidates in USA. AMD is a technology company developing products for AI, data centers, PCs, gaming, and embedded systems. The Principal Modeling Architect will lead workload modeling and performance analysis for next-generation Data Center GPUs, translating AI/ML, HPC, and data analytics workload insights into architectural requirements and performance projections. The role collaborates across architecture, silicon, software, and performance engineering teams to guide platform design and optimization.
Responsibilities
- Develop and refine workload modeling frameworks to characterize and project performance, scalability, and resource utilization for AI/ML, HPC, and data analytics workloads
- Analyze emerging model architectures (e.g., LLMs, transformer variants, graph neural networks), datatypes, and scaling methodologies to anticipate future platform requirements
- Collaborate with architecture, silicon design, software, and performance engineering teams to translate workload insights into platform-level technical requirements
- Lead benchmarking, profiling, and simulation efforts to validate architectural assumptions and guide design trade-offs
- Produce detailed workload characterization reports, performance projections, and sensitivity analyses to inform platform strategy and technical decision-making
Skills
- 12+ years of experience in workload modeling, performance engineering, system architecture, or related technical domains
- Demonstrated expertise in modeling and analyzing AI/ML, HPC, or large-scale data analytics workloads on GPU or accelerator platforms
- Deep understanding of performance modeling methodologies, benchmarking tools, simulation environments, and workload characterization techniques
- Experience collaborating across hardware, software, and system engineering teams to drive workload-informed architectural decisions
- Strong analytical, communication, and technical writing skills; ability to synthesize complex data into actionable insights
- Advanced degree in Computer Science, Electrical Engineering, or related field preferred
- This role is not eligible for visa sponsorship
- Experience with ROCm, CUDA, or other GPU programming frameworks
- Familiarity with compiler/runtime systems, kernel libraries, and developer tooling for AI/ML workloads
- Track record of publishing workload analysis or performance modeling research in peer-reviewed venues
- Experience engaging with hyperscalers, CSPs, or large enterprise customers on workload deployment and optimization
Benefits
- Bay Area or Remote
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