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
- Bachelor's degree in Engineering, Information Systems, Computer Science, or related field and 4+ years of Software Engineering or related work experience.
- OR
- Master's degree in Engineering, Information Systems, Computer Science, or related field and 3+ years of Software Engineering or related work experience.
- OR
- PhD in Engineering, Information Systems, Computer Science, or related field and 2+ years of Software Engineering or related work experience.
2+ years of work experience with Programming Language such as C, C++, Java, Python, etc.
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