Intern- Machine Learning

Title: Machine Learning Scientist Intern 

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! 

About the Opportunities 

Symbotic’s Machine Learning team is an accomplished group of engineers who are working on sophisticated algorithms in areas such as routing, perception, and computer vision.   

• Routing interns will develop algorithms to boost efficiency in a multi-agent system while ensuring scalable software deployment and adherence to production standards.  

• Perception and Computer Vision interns will evaluate and propose new projects for replacing legacy tools with vision-based algorithms. These interns will scope projects from literature review, to dataset design and aggregation, training pipeline development, all the way through production deployment. Practical projects include vision-based collision avoidance.   

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. 

• Identify high-impact scenarios where AI Planning and machine learning can be applied to optimize the Symbotic robotics software 

• Adapt state-of-the-art approaches or develop high-performance algorithms to solve real-world multi-robot problems. 

• Conduct simulations and experiments to validate the performance of the multi-robot algorithms 

• Present results in verbal and written communications internally, and in top-tier academic venues 

What you’ll need 

• Currently pursuing PhD or Masters degree 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. 

What’s good to have 

• Publications at high-impact conferences/journals (e.g., CoRL, ICLR, NeurIPS, ICML, ICLR, RSS, ICRA,  

• IROS, RAL, AAAI, ICAPS, IJCAI, JAIR, etc.) on some of the aforementioned topics 

• Experience with RGB cameras (pinhole camera model, calibrations, projections). 

• Experience with 3D sensors and point cloud processing such as PCL. 

• Experience with embedded computing such as Nvidia Jetson. 

• Experience with code revision control (git). 

• Experience with containerization (docker). 

• Experience with using the command line (bash). 

• Experience with transformation between coordinate systems (homogeneous transformations, Euler angles, quaternions). 

• Experience with cloud-hosted environments (AWS, GCP, Azure). 

Our Environment  

• Up to 10% of travel may be required. Employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations. 

• The employee is responsible for owning a credit card and managing expenses personally to be reimbursed on a bi-weekly basis.