GPU Compute & MLIR Compiler Principal Engineer - AI Workloads, Systems Engineering

Company:

Qualcomm India Private Limited

Job Area:

Engineering Group, Engineering Group > Systems Engineering

General Summary:

• Drive technical direction and execution of AI and Generative AI workloads on GPU platforms.
• Lead exploration and optimization of emerging workloads including LLMs, transformer-based models, computer vision, and other AI/ML applications.
• Collaborate across architecture, compiler technologies and frameworks such as TVM, MLIR, and IREE, software, performance, and product teams to drive solutions from early exploration through product-quality implementation.
• Provide technical leadership, conduct design reviews, and mentor engineers working across GPU AI initiatives.
• Develop and optimize GPU solutions leveraging capabilities such as matrix acceleration, mixed-precision computation, quantization, and other hardware acceleration features.
• Analyze end-to-end AI workload performance and identify bottlenecks across models, frameworks, compilers, runtimes, GPU kernels, and hardware architecture.
• Drive compiler and runtime optimizations to improve AI workload performance, power efficiency, and scalability.
• Evaluate emerging AI models, algorithms, frameworks, and optimization techniques and assess their implications for future GPU architectures.
• Work closely with GPU architecture teams to translate workload analysis into actionable recommendations for future architectural capabilities.
• Develop prototypes and proof-of-concepts to evaluate new ideas and demonstrate GPU capabilities for AI workloads.
• Communicate technical findings, trade-offs, recommendations, and results effectively to both engineering and leadership audiences

Minimum Qualifications:

•    Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 8+ years of Systems Engineering or related work experience.
OR
Master's degree in Engineering, Information Systems, Computer Science, or related field and 7+ years of Systems Engineering or related work experience.
OR
PhD in Engineering, Information Systems, Computer Science, or related field and 6+ years of Systems Engineering or related work experience.

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