Intuitive Surgical Careers
Machine Learning Intern
Primary Location: United States-Georgia-US-GA-Norcross
Requisition ID: 182834
Who is Intuitive Surgical? The numbers tell an amazing story. Learn more about our company.
Joining Intuitive Surgical, Inc. means joining a team dedicated to using technology to benefit patients by improving surgical efficacy and decreasing surgical invasiveness, with patient safety as our highest priority.
Must be concurrently enrolled and returning to an academic program in the fall in an accredited degree-seeking program.
Primary Function of Position:
We are seeking a summer intern to support the Applied Research team in the development of machine learning algorithms to drive advanced analytics and digital product development focused on optimizing surgical workflow and performance on next generation robotic surgery platforms.
Roles and Responsibilities:
• Explore and develop machine learning algorithms for spatiotemporal analysis, including spatial segmentation, temporal segmentation, and sequence labeling
• Develop tools for annotation and curation of large data sets
• Work closely with researchers, engineers, surgeons, and external academic partners and have exposure to other departments throughout the company
Skills, Experience, Education, & Training:
• M.S. or Ph.D. candidate in Computer Science, Robotics, Biomedical Engineering, Neuroscience, or similar
• Excellent problem solving abilities in a fast paced, exciting work environment
• Experience writing algorithms and code using Matlab, Python, and/or C/C++
• Experience with machine learning frameworks such as Tensorflow, Theano, and/or Caffe
• Gain experience applying algorithms to real-world data
• Learn how to work closely with engineers, surgeons, and other professionals
• Gain experience presenting research to a broad audience throughout the company
Commitment: Must be available to work full-time hours, M-F for 10-12 weeks beginning Summer of 2019.
We are an AA/EEO/Veterans/Disabled employer.
We will consider for employment qualified applicants with arrest and conviction records in accordance with fair chance laws.