ML Engineering Intern

Intuitive • Peachtree Corners, GA

10h

About The Position

Primary Function of Position We are looking for a talented individual to join our growing machine learning and data science team to help provide creative ways to develop new technology focused on surgical workflow and performance for next generation robotic surgery platforms. As a Machine Learning Engineer Intern, you will work at the intersection of machine learning and engineering (i.e., MLOps) to contribute to innovative digital solutions leveraging Surgical AI/ML technologies. You will be reporting to the Manager of MLOps Engineering within the Digital Organization at Intuitive.

Requirements

• University Hiring Program Eligibility Requirements: University Enrollment: Must be currently enrolled in and returning to an accredited degree-seeking academic program after the internship. Internship Work Period: Must be available to work full-time (approximately 40 hours per week) during a 10-12 week period starting May or June. Specific start dates are shared during the recruiting process.
• Required Education and Training Current enrollment in an Computer Science, Computer Engineering, Electrical & Computer Engineering, or related degree-seeking program at the Master’s, or Doctorate level.
• Excellent communication skills both written and verbal
• A desire to work in a high-energy, focused, small-team environment with a sense of shared responsibility and shared reward
• Interest in early research and development through to product roll-out in the fields of surgical AI and surgical robotics
• Hands-on experience with ML frameworks, such as PyTorch, Tensorflow, or similar
• Knowledgeable about MLOps platforms and/or ML CI/CD workflows to manage datasets and model training, deployment, and monitoring
• Experience with MLOps tools like MLFlow, KubeFlow, W&B, etc
• Knowledgeable on cloud compute environments such as AWS, GCP, etc
• Experience with Python and SQL
• Experience with Git e.g github, gitlab, bitbucket, etc

Nice To Haves

• Knowledgeable in launching ML models into production
• Experience supporting large multi-modality dataset including image/video
• Experience in developing ML applications within healthcare

Responsibilities

• Working closely with Machine Learning and Data/Software Engineering teams to develop efficient processes for model development/deployment for various applications.
• Developing automated workflows and tools to curate datasets and facilitate training of deep learning models
• Integrating machine learning into digital products and services by working cross-functionally across engineering, data science, and machine learning teams

What This Job Offers

• Job Type: Full-time
• Career Level: Intern