Responsibilities and Opportunities

• Design and develop compute libraries (e.g., BLAS, DNN libraries) consisting of neural network operations optimized for Rebellions’ proprietary instruction set architecture (ISA)
• Expand the functional coverage of each operation by addressing operation-specific constraints, including tensor shape variations, precision management, and numerical stability
• Improve utilization of heterogeneous compute resources by optimizing operation-level performance based on workload characteristics
• Design and implement architecture-aware custom kernels based on a deep understanding of dynamic execution patterns in generative AI workloads
• Contribute to hardware–software co-design by identifying and proposing critical features for next-generation compute architectures

Key Qualifications

• Master’s degree or higher in Electrical Engineering, Computer Science, or a related field
• In-depth understanding of computer architecture and parallel programming
• Strong understanding of neural network operations, including both high-level concepts and low-level computational dataflows
• Strong analytical, troubleshooting, and performance optimization skills
• Proficiency in C++ and Python

Ideal Qualifications

• Broad knowledge of deep learning models across multiple domains, including computer vision, natural language processing, and speech recognition
• Experience in developing high-performance custom kernels for generative AI workloads on GPUs or NPUs
• Experience in model- and layer-level optimization techniques for computational efficiency, such as sparsity, reduced precision, and layer decomposition
• Experience with architecture-specific parallel programming and hardware acceleration frameworks, including SSE/AVX (x86), NEON (AArch), and CUDA/OpenCL (GPU)
• Academic background in computer architecture is preferred