AI Compiler & Kernel Engineer

Company:

Qualcomm China

Job Area:

General Summary:

Qualcomm Cloud AI is seeking an experienced AI Compiler & Kernel Engineer to develop and optimize high-performance AI operators for large-scale cloud inference workloads. You will work across model, compiler, runtime, and hardware layers to maximize inference performance while minimizing memory footprint and resource utilization on Qualcomm Cloud AI platforms.

Key Responsibilities

• Develop and optimize AI kernels and operators for Qualcomm Cloud AI platforms.
• Enable and optimize LLM, MoE, and multimodal models for large-scale cloud inference.
• Drive accuracy, performance and efficiency optimizations across the software stack.
• Improve model memory footprint and ROM / RAM efficiency through kernel, graph, and compiler optimizations.
• Collaborate with performance and modeling teams to deliver best-in-class inference efficiency.
• Analyze and resolve performance bottlenecks using profiling, benchmarking, and modeling methodologies.

Required Qualifications

• Master's degree or above in Computer Science, Artificial Intelligence, Mathematics, Electrical Engineering, or related fields.
• 5+ years of experience in Cloud AI inference, kernel/operator development, or AI compiler technologies.
• Good verbal and written English communication skills for effective collaboration across global teams.
• Proficiency in C++, Python, and Linux development environments.
• Deep understanding of Transformer architectures, such as LLMs, MoE, and modern foundation models.
• Strong experience with compiler technologies such as MLIR, Triton, TVM, or similar frameworks, including graph transformation, operator lowering, scheduling, operator fusion, kernel auto-tuning, code generation and memory scheduling.
• Strong experience with parallel computing architectures and performance optimization techniques, including vectorization, tiling, memory hierarchy optimization, and workload scheduling.
• Strong experience with graph optimization, model accuracy validation, performance optimization, and memory optimization.

Preferred Qualifications

• Experience optimizing transformer-related operators such as Attention, FlashAttention, KV Cache, Embedding, GEMM, Softmax, and MoE routing is highly desirable.
• Hands-on experience developing custom operators and high-performance kernels, either manually or through compiler frameworks such as MLIR or Triton with AI accelerator platforms, such as GPUs, NPUs, TPUs, or custom AI hardware.

Minimum Qualifications:

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