AI Model Algorithm Engineer

Company Overview An IC design start-up founded by senior leaders from Huawei and IBM, specializing in advanced wireless ICs including UWB, BLE, NB-IoT, and WiFi6, with a strong track record in wireless IC development and mass production. Job Summary Lead the optimization and deployment of AI audio and video models for resource-constrained IoT devices, driving hardware-aware acceleration and end-to-end performance improvements for next-generation AIoT chips. Responsibilities Lead end-to-end optimization of audio (wake-word detection, ASR, acoustic event detection) and video (object detection, recognition, segmentation) AI models for resource-constrained IoT devices to improve efficiency and accuracy Apply quantization techniques (INT8/INT4/mixed-precision, PTQ & QAT), pruning (structured/unstructured), knowledge distillation, and other compression methods to reduce model size, latency, and power consumption while maintaining accuracy Optimize models for heterogeneous hardware platforms (NPU, DSP, MCU) using frameworks such as RKNN, Hexagon, Ethos, and TFLite Micro to enhance hardware performance Develop custom operators, implement operator fusion, and perform memory planning and cache optimization to maximize hardware utilization Conduct hardware-aware quantization and Neural Architecture Search (NAS) to tailor models for specific AIoT chip architectures Build automated pipelines for model optimization and deployment to streamline production workflows Optimize multimodal (audio + video) models for real-world IoT applications, ensuring best-in-class latency, throughput, power consumption, and memory footprint on production devices Research and implement state-of-the-art techniques in model compression, quantization, and dynamic inference to maintain technological leadership Collaborate closely with hardware and system teams to enable software-hardware co-design for next-generation AIoT chips Required competencies and certifications Master’s degree or higher in Computer Science, Artificial Intelligence, Electronic Engineering, or related fields Minimum 3 years of hands-on experience in on-device AI model optimization with successful production deployments Proven expertise in at least 2–3 of the following areas: quantization (PTQ, QAT, mixed precision), pruning (structured & unstructured), knowledge distillation (single/multi-teacher, feature distillation), model compression (sparsity, low-rank decomposition), dynamic inference (Early Exit, BranchyNet, conditional computation), hardware acceleration (NPU/DSP optimization, operator fusion) Proficiency in AI frameworks such as TFLite / TFLite Micro, ONNX Runtime, TensorRT, RKNN, SNPE, or similar Strong programming skills in C/C++ and Python Preferred competencies and qualifications Experience optimizing audio models (KWS, ASR) or vision models (YOLO, MobileNet, EfficientNet, etc.)