Responsibilities and Opportunities

• Performing functional verification and debugging focusing on enhancing the stability of RBLN SDK
• Performing device profiling and optimization focusing on enhancing the performance of RBLN SDK
• Designing internal/external SDK verification utilities, including performance profiler/debugger, model partitioning/feeding frameworks, etc.

Key Qualifications

• Bachelor's or Master's degree in Computer Science, Electrical Engineering, or a related field
• Thorough knowledge of deep learning models for various applications, including vision, language, speech, etc.
• Familiarity with system software, including compiler, runtime, driver, firmware, etc.
• Proficiency in programming languages: Python, C++

Ideal Qualifications

• Experience in converting models for deployment on specific hardware, such as TF/PyTorch to ONNX, ONNX to TensorRT/OpenVINO, TF to TFLite, etc.
• Experience in porting and accelerating deep learning models on specific hardware, including x86 CPUs with SSE/AVX instructions, ARM CPUs with NEON instructions, heterogeneous computing on SoCs with CPU, GPU, NPU, and DSP, etc.