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.