NPU Research Engineer

A leading global technology company driving innovation in AI acceleration and next-generation computing architectures. The organization develops high-performance hardware-software solutions focused on neural processing units (NPUs) and advanced compiler systems. Their products power large-scale AI systems across industries such as cloud computing, edge AI, and high-performance research infrastructure.

As an NPU Compiler & Framework Engineer, you will lead the co-design of AI frameworks and compiler tool chains tailored for NPU acceleration. Your work will directly impact how large AI models are executed with maximum efficiency, leveraging low-level optimizations, intelligent scheduling, and hardware-software synergy. This role is at the intersection of systems design, compiler architecture, and AI infrastructure, with strong visibility in both academic and open-source communities.

• Design and implement smart cross-layer optimizations for AI compilers and runtimes (e.g., PyTorch, vLLM) targeting NPU workloads.
• Automate model transformation, quantization, and adaptive deployment for optimized execution on custom NPU hardware.

• Extend and optimize compiler stacks (LLVM, TVM, GCC) to translate high-level AI models into high-performance NPU code.
• Focus on scheduling strategies, memory management, and parallel execution tailored to NPU microarchitectures.

• Collaborate with hardware teams to define ISA extensions, performance counters, and architectural features that enable better software-level optimization.
• Participate in shaping the future of AI accelerators through feedback-driven development.

• Publish results in top-tier systems and machine learning conferences (e.g., ISCA, ASPLOS, MLSys).
• Support the developer ecosystem with documentation, tooling, and contributions to open-source AI compiler projects.

• Master’s degree in Computer Science, Computer Engineering, or related field, plus 3 years of experience in systems software; or a recent PhD in a relevant domain.
• Solid knowledge of compiler internals (LLVM, GCC, TVM, XLA) and modern NPU/GPU architecture.
• Strong programming skills in Python and C/C++ for system-level development.
• Excellent communication skills in English, both written and spoken.

• Hands‑on experience with AI frameworks such as PyTorch or Tensor Flow.
• Familiarity with model optimization techniques, quantization, and graph transformation.
• Experience working with DSP/xPU tool chains or specialized accelerators.
• Open‑source contributions or publications in relevant conferences.