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Intern - Machine Learning (Computer Vision)
Symbotic,
Who we are
With its A.I.-powered robotic technology platform, Symbotic is changing the way consumer goods move through the supply chain. Intelligent software orchestrates advanced robots in a high-density, end-to-end system – reinventing warehouse automation for increased efficiency, speed and flexibility.
What we need
We want to learn from you! Our teams at Symbotic value the early career talent that helps us solve critical issues in creative and innovative ways. That is why we offer Internship and Co-op programs where students have the opportunity to work alongside industry professionals on some of our most pivotal projects. We invite students from institutions across the country to apply online or engage with our teams on campus at career fairs and other events. Symbotic offers an extensive calendar of events including Lunch and Learns, Social Outings, and Volunteer opportunities for our Interns and Co-ops to engage in.
The Summer Intern session is a 12-week, full-time (40 hours Monday-Friday) program while the Co-op cohorts typically run 16-weeks in the Spring and Fall. In both programs, students are paid a competitive hourly rate. Continue reading to learn more about this exciting opportunity!
What You’ll Do:
- Research and experiment with traditional computer vision methods and deep learning methods for object detection, classification, segmentation, pose estimation, anomaly detection on images or videos
- Evaluate and benchmark computer vision methods on production data
- Deploy software to production on robot hardware
- Design data pre and post processing methods
- Document designs, code, experiments, evaluations
What You’ll Need:
- Currently pursuing bachelor degree or above in Computer Science or other related discipline required
- Strong programming skills in Python and/or C++ (Python is preferred)
- Experience with ML frameworks such as PyTorch and/or Tensorflow (PyTorch is preferred)
- Experience with image processing such as OpenCV
- Enjoys learning new technologies, skills, and techniques as well as teaching them to others