Location: Palo Alto, CA

Key Responsibilities: 

• Research and develop advanced real-time robotic SLAM (Simultaneous Localization and Mapping) solutions including 3D sensing, position tracking, 3D mapping / reconstruction, and sensor fusion (RGB-D and inertial sensors).

• Research and develop practical solutions for 3D scene understanding (e.g., object detection) using state-of-the-art deep learning (DL) and machine learning (ML) methods.

• SDK development to support applications utilizing the above solutions.

• Analyze and optimize computer vision and deep learning algorithms on mobile/embedded devices, e.g., using hardware acceleration such as DSP/GPU.

• Investigate and enable various perception sensors on Android/Linux platforms.

• We encourage publishing papers to top-tier conferences such as CVPR / ICCV / ECCV / ICML / NIPS / ICRA / IROS / ISMAR.

Location: Palo Alto, CA

Key Responsibilities: 

• Develop camera algorithms and use cases with ISP, computer vision and AI for smartphone platforms. Image quality improvement with traditional ISP or CV/AI methods. 

• Work with the Camera and AI team leads to push new photography user experience by working with software, research, product teams across the company. 

• Understand and analyze the effects of the interplay between hardware and software architectures on performance, efficiency and optimization of algorithms and applications. 

Location: Palo Alto, CA

Key Responsibilities: 

• Work in a collaborative environment to develop cutting-edge deep learning algorithms and software. 

• Engage in cutting-edge research in computer vision and deep learning to solve real-world problems for mobile use cases. 

• Ideate, prototype, test and launch future technologies.

Location: Palo Alto, CA

Key Responsibilities: 

• Develop innovative software architectures to build, analyze and prototype key deep learning and data analytic algorithms and applications for smartphone platforms. 

• Understand and analyze the effects of the interplay between hardware and software architectures on performance, efficiency and optimization of algorithms and applications.

• Work with internal AI team lead to guide the technical direction of machine learning by working with software, research, product teams across the company.