This role is open across our global offices, and successful candidates may be based in any of our international office locations.

Key Responsibilities

• Develop and optimize compiler components for AI workloads (e.g., graph optimizations, operator fusion, scheduling).
• Collaborate with hardware and software teams to design compiler backends for NPUs, and custom accelerators.
• Implement performance tuning techniques for deep learning models across diverse architectures.
• Contribute to open-source compiler projects and maintain internal toolchains.
• Analyze and improve compilation pipelines for frameworks like PyTorch, TensorFlow, and ONNX.

Required Qualifications

Master’s degree or PhD in Computer Science, Electrical Engineering, or related field.

Strong proficiency in C++, Python, and compiler design principles.

2 out of 3 below,

• Knowledge of AI frameworks (PyTorch, TensorFlow)
• Familiarity with hardware architectures (GPU, TPU, NPU) and parallel computing.
• Understanding of graph-based optimizations.

Preferred Qualifications

Experience with quantization, kernel optimization, and code generation for AI accelerators.

Experience with MLIR, LLVM, or similar compiler infrastructures.

Contributions to open-source compiler or AI projects.

Understanding of performance profiling and benchmarking for AI workloads.