We are seeking a skilled software engineer to join our NPU software stack development team. This role involves developing high-performance GPU programming frameworks, runtime systems, and libraries for AI/ML workloads. You will be responsible for implementing, optimizing, and maintaining GPU software stack components to support distributed AI training and inference.
Key Responsibilities:
Identify bottlenecks, analysis and optimize in distributed NPU eco-system
Design and develop NPU memory management system
Design and develop optimized NPU development framework, execution path and debugging
Develop compatibility with AI frameworks (Triton, PyTorch, JAX)
Write high-quality, well-tested code with comprehensive documentation
Collaborate with other teams (Hardware, Network, QA, AI Framework Integration)
Participate in code reviews and technical design discussions
Requirements: 5+ years of experience in distributed system programming
3+ years of experience with NPU programming (Triton, CUDA, HIP, OpenCL)
Expert-level C/C++ programming with focus on performance optimization
Expert-level Python programming with focus on DL/ML frameworks (PyTorch/JAX/etc)
Deep understanding of NPU architecture, memory tiering, and programming models
Knowledge of NPU runtime systems
Experience with performance profiling and optimization tools
Strong problem-solving and debugging skills
Experience with version control systems, Ticking system and collaborative development
Team player with excellent communication skills
Fast learner, highly organized, detail-oriented with high motivation
Preferred Qualifications:
Experience with NPU software stack development
Experience with large-scale NPU systems (100+ NPUs)
Experience with DL/ML workloads (oriented AI) and distributed training / inferencing
Familiarity with containerization and orchestration
This position is open to all candidates.