Machine Learning Engineer I

Join CNN's digital team in New York, NY onsite as a Machine Learning Engineer I to build and deploy production ML systems powering personalization, search, recommendations, and content understanding across CNN's digital platforms.

Responsibilities

• Develop and deploy end-to-end machine learning solutions in Python for CNN's digital products, covering personalization, search, recommendations, and content understanding.
• Design and maintain production ML pipelines, including feature engineering, model training, evaluation, and serving infrastructure.
• Establish rigorous experimentation and A/B testing frameworks to quantify model performance and product impact.
• Tune ML systems for real-time, web-scale performance serving millions of users.
• Collaborate with platform and infrastructure teams to ensure ML services meet reliability, scalability, and performance standards.
• Participate in code reviews, document ML workflows, and share knowledge across the team.

Requirements

• Graduate degree (MS or PhD) in computer science, mathematics, statistics, engineering, or another quantitative field.
• At least 1 year of professional experience building and deploying ML systems in production.
• Proficiency in Python and experience with ML frameworks such as scikit-learn or equivalent.
• Experience across the full ML lifecycle, including data preprocessing, feature engineering, model training, evaluation, and deployment.
• Solid understanding of software engineering best practices, including version control, testing, and CI/CD.
• Ability to collaborate effectively with cross-functional partners.
• Strong communication skills, with the ability to explain technical concepts to non-technical stakeholders.

Technologies

• Python
• SQL
• scikit-learn
• Git
• MLflow
• REST APIs
• Docker
• Kubernetes
• AWS
• GCP
• Azure

Benefits

• Health insurance coverage
• Employee wellness program
• Life and disability insurance
• Retirement savings plan
• Paid holidays
• Sick time
• Vacation
• Annual bonuses
• Short-term incentives
• Long-term incentives
• Program-specific awards