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.)