Computer Vision and Machine Learning Engineer, Senior at Qualcomm Technologies, Inc.

Department: Machine Learning Engineering

Language

Setup: On-site
Location: Hsinchu
Level: senior
Posted: Aug 26, 2026

Description

Qualcomm Semiconductor Limited is hiring a Computer Vision Research Engineer for its Multimedia R&D Group in Hsinchu, Taiwan. The role develops computer vision and machine learning algorithms, architectures, and systems for embedded smartphone, IoT, PC, XR, and other platform products; creates proof-of-concept demonstrations on Qualcomm platforms; improves accuracy and efficiency for commercial applications; tracks emerging techniques; and proposes commercialization and patent opportunities. Candidates need a relevant bachelor's degree with 2+ years of related experience, a master's degree with 1+ year of related experience, or a PhD, along with hands-on computer vision research, deep learning model design and training, and C/C++/Python proficiency.

Required Skills

Computer vision
Deep learning
C/C++/Python
Vision models
Model compression
Qualcomm SDKs

Additional Information

Minimum Education: Bachelor's degree in a relevant field with 2+ years of related experience, or a master's degree with 1+ year of related experience, or a PhD in a relevant field.
Required Previous Experiences: Hands-on computer vision research in areas such as object detection, action recognition, vision foundation models, 3D reconstruction, biometrics, or deepfake detection.
Experience designing, training, and fine-tuning deep learning models.
Proficiency in C/C++/Python.
2+ years of hardware, software, systems, or related engineering experience for bachelor's-degree candidates.
1+ year of hardware, software, systems, or related engineering experience for master's-degree candidates.
Preferred Qualifications: Experience with deep learning model compression, quantization, and on-device deployment.
Experience with computer vision software development.
Experience with Qualcomm hardware platforms.
Experience with Qualcomm AI SDKs and tools.
Experience with camera frameworks and sensor-related image processing.
Experience optimizing performance on ARM, DSP, GPU, or NPU hardware.
Experience with cross-platform development across Windows, Android, and Linux.