Computer Vision Intern

B GARAGE was founded in 2017 by a Ph.D. graduate from Stanford University. After having spent over five years researching robotics, computer vision, aeronautics, and drone autonomy, the founder and team set their minds on building a future where aerial robots would become an integral part of our daily lives without anyone necessarily piloting them. Together, our common goal is to redefine the user experience of drones and to expand the horizon for the use of drones.

Roles and Responsibilities

• Able to develop perception for aerial robots and inventory recognition for warehouse by leveraging computer vision and deep learning techniques

• Aid the computer vision team to deliver prototype and product in a timely manner

• Collaborate with other teams within the company

Minimum Qualifications

• PhD student in computer science, robotics, electrical engineering or other engineering disciplines

• Proficient in image processing algorithms and multiple view geometry using camera

• Proficient in machine learning architectures for object detection, segmentation, text

  recognition etc.

• Proficient with ROS, C++, and Python

• Experience with popular computer vision and GPU frameworks/libraries (e.g., OpenCV,

  TensorFlow, PyTorch, CUDA, cuDNN etc.)

• Experience with robots operating on real-time onboard processing

• Self-motivated person who thrives in a fast-paced environment

• Good problem solving and troubleshooting skills

• Legally authorized to work in the United States

Optional Qualifications

• Publish research papers in CVPR, ICCV, ECCV, ICRA, IROS, etc.

• Experience with scene reconstruction, bundle adjustment and factor graph optimization

  libraries

• Experience with Javascript and massively parallel cloud computing technologies

  involving Kafka, Spark, MapReduce

• Proficient in containerization technologies (Docker, Kubernetes) and container

• orchestration technologies

• Experience in cloud computing platforms (AWS, GCP, etc.)

• Experience with rapid prototyping for low-volume production in either professional and/

  or academic environment is a plus