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Computer Vision Research Engineer, Object Recognition and Pose Estimation - Internship/Fellowship Program

Our company vision is to amplify human potential. Our mission is to deliver enterprise a powerful tool for transformation—an augmented reality platform of great utility and simplicity. Achieving our goals requires passion and dedication. That’s why we’re committed to building and empowering a diverse team of incredibly talented people and fostering an inclusive culture through our values of unity, innovation, and user centricity.

Magic Leap is looking for PhD, Masters and Bachelors students to join our team for a 3 month Summer Program for Underrepresented Students in Engineering, this program is based in Zurich, Switzerland.

Job Description

You will work fully integrated in Magic Leap’s perception team and help shape the perception systems of our newest generation devices.

Depending on your interest and skills you will work on object recognition and pose estimation in the area of:

• Improvement of object pose estimation pipeline in terms of precision and inference speed with state-of-the-art techniques
• Investigate object pose estimation pipelines with different input modalities (intensity, depth, IMU)
• Temporal object recognition, tracking and pose estimation
• Direct pose estimation by leveraging the coarse object reconstruction mesh or CAD
• Few-shot learning for object pose estimation with semi-supervised and self-supervised learning
• Investigation of customer guided data acquisition to enable better object pose estimation on the limited collected data
• Creating mesh reconstruction and automatic pose estimations on new object models by Neural Radiance Field data generation

Must-Haves

• You have just finished or are finalizing your MSc or PhD degree (preferred) in Computer Science , Robotics, Electrical Engineering or equivalent
• Fluent in Python and C/C++
• Deep Learning, Computer Vision and 3D geometry knowledge
• Projects in Object Recognition, Panoptic Segmentation, Object Pose Estimation in 2D and 3D, and geometric reconstruction

Nice to have

• Deep Learning using unsupervised and self-supervised approaches
• Geometric understanding and rendering with Neural Readiance Fields (NeRF)
• Software optimization, embedded programming, and parallel computing (e.g. SIMD, OpenCL, GPGPU, etc.)

Education

• MSc or PhD degree (preferred) in Computer Science , Robotics, Electrical Engineering or equivalent