Engineer II, Machine Learning Ops CBM Lab

About the role

The Machine Learning Engineer II will actively participate in deploying, monitoring, and scaling machine learning models in production and big data research. They will evaluate data sets to determine their suitability for applying machine learning models and techniques, guide and assist with the collection and curation of clinical datasets, assist in the implementation and evaluation of machine learning algorithms, develop and maintain continuous integration and continuous deployment pipelines for automated training and deployment of machine learning models, manage machine learning infrastructure and optimize resource utilization, implement monitoring solutions for model performance and health, lead small projects or initiatives related to machine learning operations, and advocate for best practices in machine learning operations within the team.

Responsibilities

• Evaluate data sets for suitability of machine learning models
• Guide and assist with the collection and curation of clinical datasets
• Aid in the implementation and evaluation of machine learning algorithms
• Develop and maintain continuous integration and continuous deployment pipelines
• Manage machine learning infrastructure and optimize resource utilization
• Implement monitoring solutions for model performance and health
• Lead small projects or initiatives related to machine learning operations
• Advocate for best practices in machine learning operations within the team

Requirements

• Professional level of knowledge in computer science, engineering or a related field, typically acquired through a Bachelor’s Degree
• Minimum of 3 years of related experience working on problems of moderate scope where analysis of situations or data requires a review of a variety of factors
• Continues to develop professional expertise, applying institute policies and procedures to resolve a variety of issues

Skills & Abilities

• Proficient in using version control systems, especially Git
• Able to manage branches, handle merge conflicts, and understand the importance of commit history and reverting changes
• Strong skills in Python and experience with machine learning frameworks
• Familiarity with tools for deploying machine learning pipelines (e.g. Docker, Kubenates)
• Familiarity with cloud based production pipelines offered by leading manufacturers (e.g. Microsoft Azure, Amazon Web Services, Google Cloud, etc.)
• Working independently on assigned tasks and leading small projects
• Excellent problem-solving skills and the ability to troubleshoot complex issues
• Good communication skills in both written and verbal forms
• Able to work with research subjects and clinicians in a clinical setting
• Able to take direction and complete defined tasks in addition to anticipating and executing follow-up actions
• Able to perform assignments by receiving general instructions on routine work, and detailed instructions on new projects or assignments
• Able to exercise judgment within defined procedures and practices to determine appropriate action
• Able to build stable working relationships with multidisciplinary team