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AI Vision Processors For Edge Applications Our solutions make cameras smarter by extracting valuable data from high-resolution video streams.
Job Description Responsibilities Optimize CNN, Transformer, and related models for our hardware architecture and deployment constraints. Build conversion tooling from PyTorch / ONNX to chip-executable representations, supporting automated model deployment pipelines. Develop an edge AI runtime covering model loading, task scheduling, and memory management, with multi-task parallel inference. Support customer model porting and performance tuning, and deliver tailored AI solutions where needed. Contribute to technical documentation and standards: write specifications and best practices, and help the team capture and share technical knowledge. Requirements Bachelor’s degree or above in Computer Science, Electrical Engineering, or a related field, with 2+ years of experience in embedded AI development. Strong C/C++ and Python; hands-on embedded system development and debugging. Solid familiarity with deep learning frameworks (PyTorch / ONNX) and the end-to-end model deployment workflow. Understanding of CNN and Transformer architectures; model optimization or operator/kernel development experience is a plus. Experience in edge AI deployment, LLM serving / efficiency (e.g. vLLM-class stacks), or AI agent development is a plus. Strong cross-team collaboration and problem-solving; ability to narrow down and resolve complex technical issues quickly.
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