Engineer II, Machine Learning Ops CBM Lab

The Machine Learning Engineer II Will

• Actively participates in deploying, monitoring, and scaling machine learning models in production and big data research.
• Evaluate data sets to determine 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 optimizes resource utilization.
• Implement monitoring solutions for model performance and health.
• Lead small projects or initiatives related to machine learning operations.
• Work collaboratively with data scientists to optimize model performance.
• Advocate for best practices in machine learning operations within the team.
• Participate in maintaining a safe work environment through adherence to policies and procedures relative to safety, fire prevention, hazard communications, security, equipment use and maintenance, infection control and vehicle safety.
• Perform all other duties that may be assigned in the best interest of the Shirley Ryan AbilityLab.

Reporting Relationships

• Reports directly to a designed engineering manager.

Knowledge, Skills & Abilities Required

• A 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.
• 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.
• Working proficiency with Linux and Windows operating systems.
• Familiarity with tools for deploying machine learning pipelines (eg Docker, Kubenates).
• Familiarity with cloud based production pipelines offered by leading manufacturers (eg Microsoft Azure, Amazon Web Services, Google Cloud, etc).
• Ability to work independently on assigned tasks and lead 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.

Working Conditions

• Normal office environment with little or no exposure to dust or extreme temperature.

The above statements are intended to describe the general nature and level of work being performed by people assigned to this classification. They are not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified.