Embedded Computer Vision Engineer

Role Summary

We are looking for an experienced Embedded Computer Vision Engineer to join our growing AI team. In this role, you will work across the full lifecycle of AI-powered camera products — from training and optimizing deep learning models, to deploying them on embedded camera platforms with NPU acceleration, to building the on-device applications that enable intelligent camera features. You will collaborate closely with software, firmware, and hardware teams to deliver high-quality, production-ready AI camera solutions.

Responsibilities (What he/she will do after joining UI)

• Develop and maintain on-camera AI application pipelines, including object detection, event triggering, tracking, and camera peripheral control
• Port, convert, and optimize deep learning models for deployment across multiple SoC/NPU platforms while maintaining accuracy and real-time performance
• Integrate and validate AI inference engines with vendor-specific NPU SDKs and toolchains such as ONNX, TensorRT, QNN, or similar frameworks
• Collaborate with hardware and firmware teams on New Product Introduction (NPI) for next-generation AI cameras, from early SoC evaluation through production bring-up
• Train, fine-tune, and benchmark computer vision models for tasks such as detection, classification, action recognition, and video analytics
• Profile and debug performance bottlenecks across CPU, GPU, and NPU to meet latency, throughput, and power constraints
• Build and improve reusable frameworks, tools, and CI/CD pipelines for model conversion, testing, and deployment at scale
• Contribute to technical direction and system design for AI camera platforms; candidates with leadership experience may also help guide project execution and mentor engineers

• MS or PhD in Computer Science, Electrical Engineering, or a related field or equivalent practical experience
• 3+ years of professional experience in edge AI system deployment
• Strong proficiency in Python and C/C++
• Hands-on experience with at least one deep learning framework such as PyTorch or TensorFlow
• Experience with model optimization techniques such as quantization, pruning, knowledge distillation, or compression for resource-constrained devices
• Strong English communication skills, with the ability to collaborate effectively across international and cross-functional teams

• Experience in computer vision or NLP, including deep learning applications such as action recognition, anomaly detection, or video analytics
• Experience deploying deep learning models on embedded or edge platforms with NPU or AI accelerator hardware such as Ambarella, Qualcomm, InnoFusion, MediaTek, or similar SoCs
• Familiarity with model conversion toolchains and runtime engines such as TensorRT, ONNX Runtime, or SNPE
• Hands-on experience in NPI or product development lifecycle for camera or IoT hardware
• Familiarity with Large Language Models (LLMs) and Vision Language Models (VLMs) is a plus
• Experience with CI/CD, automated testing, or MLOps for edge AI deployment is a plus
• Demonstrated project ownership or mentoring experience is a plus
• Self-motivated, collaborative, and comfortable working in a fast-paced environment with challenging technical problems