Senior Member of Technical Staff- Machine Learning & AI

Senior Machine Learning Engineer

About you:

You love to own important work and find it difficult to turn down a good challenge. You are excited about the latest developments in ML and keep abreast of the latest methods and technologies. You have experience building and evaluating ML models. You have strong communication skills and can work with colleagues from a variety of technical and non-technical backgrounds. Finally, you have a strong interest in improving healthcare.

About the team

The athenaCollector product is a vital component of the athenaOne platform, processing billions in client revenue annually. The Data Science team is bringing machine learning to bear against the hardest problem in revenue cycle management in healthcare. We are working with other engineering leaders in athenaCollector product to build machine learning into our Best in KLAS suite of products to drive innovation and increase automation. Also, this team is responsible to deploy state-of-the-art machine learning models using cloud technologies.

Job Responsibilities

As a member of the athenaCollector Data Science team, you are expected to work on projects within scrum teams of 2-4 people and execute the following:

• Identify opportunities for different machine learning techniques and evaluate which are best
• Assist in developing and deploying ML-based production services to our clients
• Understand and follow conventions and best practices for modeling, coding, architecture, and statistics; and hold other team members accountable for doing so
• Apply rigorous testing of statistics, models, and code
• Contribute to the development of internal tools and AI team standards

Typical Qualifications:

• Bachelors or Masters in relevant field: Math, Computer Science, Data Science, Statistics, or related field
• 4-8 years of professional hands-on experience in developing, evaluating & deploying machine learning models
• Experience with Python, SQL , Unix
• Experience with development and implementation of Deep Learning Models with complex neural network architectures is a bonus
• Experience with training & fine tuning LLMs & GenAI models is a bonus.
• Familiarity with NLP or computer vision techniques
• Experience using the AWS ecosystem a bonus, including Kubernetes, Kubeflow or EKS experience
• Excellent verbal communication and writing skills