AI Model Algorithm Engineer

Company OverviewAn 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 SummaryLead 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.ResponsibilitiesLead 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 accuracyApply 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 accuracyOptimize models for heterogeneous hardware platforms (NPU, DSP, MCU) using frameworks such as RKNN, Hexagon, Ethos, and TFLite Micro to enhance hardware performanceDevelop custom operators, implement operator fusion, and perform memory planning and cache optimization to maximize hardware utilizationConduct hardware-aware quantization and Neural Architecture Search (NAS) to tailor models for specific AIoT chip architecturesBuild automated pipelines for model optimization and deployment to streamline production workflowsOptimize multimodal (audio + video) models for real-world IoT applications, ensuring best-in-class latency, throughput, power consumption, and memory footprint on production devicesResearch and implement state-of-the-art techniques in model compression, quantization, and dynamic inference to maintain technological leadershipCollaborate closely with hardware and system teams to enable software-hardware co-design for next-generation AIoT chipsRequired competencies and certificationsMaster’s degree or higher in Computer Science, Artificial Intelligence, Electronic Engineering, or related fieldsMinimum 3 years of hands-on experience in on-device AI model optimization with successful production deploymentsProven 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 similarStrong programming skills in C/C++ and PythonPreferred competencies and qualificationsExperience optimizing audio models (KWS, ASR) or vision models (YOLO, MobileNet, EfficientNet, etc.)