Compiler Engineer

We are looking for a world-class compiler and performance optimization expert to join our deep learning infrastructure team. You will take ownership of building a high-performance Triton compiler and kernel optimization framework, driving the next generation of AI workloads on NPUs. This is a highly technical role that sits at the intersection of AI compilation, NPU programming, and system performance engineering. Key Responsibilities Lead the design and development of the Triton compiler and performance optimization framework, enabling high-performance operator implementations on NPUs.Implement state-of-the-art Triton kernels (e.g., Attention, MatMul, LayerNorm, Conv, Softmax) with best-in-class efficiency.Optimize memory access patterns and parallel scheduling, deeply understanding cache behavior, register allocation, and SM occupancy limits.Drive end-to-end performance optimization by integrating Triton with framework backends (e.g., PyTorch, XLA) and runtime stacks..Research and apply auto-tuning, kernel fusion, and operator scheduling technologies to maximize performance scalability.Mentor team members in Triton kernel development and establish standard processes for performance analysis and optimization.Stay on top of cutting-edge compiler technologies (MLIR, TVM, Hidet, Cutlass) and introduce innovative ideas to push performance boundaries.Conduct performance modeling and workload fingerprinting for large models (LLM, Diffusion, etc.) to guide system-level optimization. What We’re Looking For Master’s degree or above in Computer Architecture, Compiler Theory, High Performance Computing, or related field; PhD preferred.5+ years of experience in NPU/GPU programming, operator optimization, or compiler development.Deep understanding of accelerator architectures and performance bottleneck analysis (compute units, vector lanes, memory hierarchy, caches, etc.)..Proficiency in Triton, PTX, or LLVM IR for low-level programming and optimization.Familiarity with PyTorch, TensorFlow, or JAX, and their graph execution and operator scheduling mechanisms.Proven ability to independently develop, benchmark, and optimize complex kernels.Skilled with performance profiling tools (e.g., perf, torch.profiler, and other vendor-neutral or runtime profilers) for quantitative analysis and performance modeling.Strong system design and software engineering skills, balancing performance, maintainability, and generality. Bonus Points Open-source contributions to Triton, LLVM, TVM, MLIR, or PyTorch.Experience with AI training or inference systems such as TensorRT, vLLM, DeepSpeed, OneFlow, or OpenXLA.Publications or patents in kernel fusion, memory tiling, or async pipeline optimization.Experience with distributed inference optimization (tensor/pipeline parallelism, ZeRO, PagedAttention).Proven cross-platform optimization experience across different accelerator vendors and architectures.