GPU Performance Modelling Architect

Company:

Job Area:

General Summary:

Job Description

Drive architecture exploration and performance analysis for next-generation GPUs through modeling, simulation, and workload characterization. Partner with architecture, software, compiler, and hardware teams to influence design decisions using data-driven insights.

Key Deliverables

• Develop and maintain GPU performance models for architecture evaluation.
• Conduct Performance analysis of graphics and compute workloads to identify bottlenecks.
• Perform architectural trade-off studies to generate and defend performance projections.
• Bring up new workloads on Performance Models by fixing bugs and developing new features.
• Evaluate new features and provide recommendations to improve performance and efficiency.
• Build automation and analysis tools to improve modeling productivity.

Responsibilities

• Design and execute performance experiments and simulations.
• Conduct root-cause analysis and derive actionable architectural insights.
• Collaborate with cross-functional teams on feature development and optimization.
• Develop tools, workflows, and methodologies for performance analysis.
• Present findings and influence future architecture decisions.

Critical / Must-Have Skills

• Strong understanding of computer architecture
• Strong analytical, debugging, and problem-solving skills.
• Proficiency in C/C++ and Python.
• Experience with architectural simulators and performance prediction.
• Experience with performance analysis, profiling, and workload characterization.
• Strong verbal and written communication skills

Good-to-Have Skills

• GPU/CPU/Memory architecture and/or Graphics fundamentals.
• Good knowledge of modern 3D graphics pipelines and APIs (Vulkan/DX12/OpenGL).
• Experience building performance analysis tools and automation frameworks.
• Technical leadership and cross-functional collaboration experience.

Minimum Qualifications:

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Systems Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Systems Engineering or related work experience.

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