GPU AI Performance Engineer

Company

Qualcomm India Private Limited

Job Area

Engineering Group, Engineering Group > Systems Engineering

General Summary

We are looking for a GPU AI Performance Engineer to help design, optimize, and accelerate AI workloads on GPU-based platforms. This role focuses on improving the performance, memory efficiency, and scalability of modern AI models — especially LLMs, transformer workloads, MoE models, and computer vision pipelines — by working across the stack from model architecture to compiler/runtime integration and low-level kernel optimization.

Responsibilities

• Analyze and evaluate GPU architecture/microarchitecture for performance optimizations of AI workloads
• Work across the AI stack, from model graphs and inference runtimes down to GPU kernels and compiler IR
• Collaborate with hardware, software, and ML teams to identify performance bottlenecks and propose architectural or algorithmic optimizations
• Analyze AI workload characteristics and correlate their behavior across different GPU generations
• Contribute to architectural trade-off studies and influence GPU roadmap decisions with data-driven insights
  
  
  

Preferred Skills

• Strong understanding of CPU/GPU architecture
• Experience in Python, C++, and ML frameworks (e.g., TensorFlow, PyTorch)
• Skills: C/C++ Programming Language, Scripting (Python/Perl), Assembly, Verilog/SystemVerilog
• Familiarity with AI inference runtimes and deployment stacks for model compilation, optimization, and execution on GPU/accelerator platforms
• Good understanding of common neural network layers and operations, including what they do and how they affect model behavior and performance
  
  
  

Nice To Have

• Experience with LLM inference engines such as llama.cpp
• Exposure to Triton, TTIR, TTGIR, or GPU compiler pipelines
• Knowledge of quantization formats and tradeoffs
• Understanding of MoE architectures
• Experience with GPU driver and compiler development
• Experience with OpenCL or Cuda development
  
  
  

Minimum Qualifications

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

OR

Master's degree in Engineering, Information Systems, Computer Science, or related field and 5+ years of Systems Engineering or related work experience.

OR

PhD in Engineering, Information Systems, Computer Science, or related field and 4+ years of Systems Engineering or related work experience.

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