Health at Scale is the market leader in precision health -- digital health programs that offer smart, hyper-personalized insights to help individuals choose the best providers, treatments, care settings and lifestyle choices for their unique healthcare needs. Founded by leading machine learning and clinical faculty from MIT, Stanford, Harvard and the University of Michigan, we work with some of the largest payers, employers and providers in the U.S. to improve outcomes, costs, access, and equity for their members. Health at Scale operates some of the largest deployments of AI in healthcare to date, covering millions of lives in production settings. We have been recognized for our innovation and as one of the fastest growing private companies by Forbes, Fast Company, Inc 500, UCSF Digital Health, Becker's, TechCrunch, Bloomberg and MIT News. For more information, please visit our website.

As a machine learning engineer at Health at Scale, you will work with an exceptional team of engineers, scientists, and clinicians to design, engineer, test, deploy and maintain machine intelligence platforms and applications for real-world production use. You will work closely to iterate and improve upon the machine intelligence technologies in each of our products. You will be the point person for translating machine learning innovations into impactful products with new customers and transforming leading-edge ideas into production-ready, real-time solutions that will serve millions of users.

• Design, engineer, test, deploy and maintain machine intelligence platforms and applications for real-world production use at scale
• Improve the accuracy, runtime, scalability and reliability of machine intelligence algorithms and software
• Develop and implement machine intelligence platform APIs for multiple use-cases
• Drive architecture of platform and application capabilities embedding machine intelligence
• Collaborate with machine learning scientists and data scientists to develop prototyped solutions and translate leading-edge ideas into production-ready systems
• Work with data engineers to ensure seamless interactions between data pipelines and machine learning pipelines in development and production environments

• BS, MS or PhD in Computer Science or related technical field
• 2 years of experience with machine learning and data science in academia or industry
• Strong understanding of the foundational concepts of machine learning and artificial intelligence
• Strong proficiency in Python (preferred), Java or C/C
• Experience in cloud computing, parallel/distributed computing and workflow management
• Excellent communication skills

Health at Scale is an equal opportunity employer and is committed to diversity in its hiring and business practices. To all recruitment agencies: Health at Scale does not accept agency resumes. Please do not forward resumes to this job alias, company employees or any organization location. Health at Scale is not responsible for any fees related to unsolicited resumes.