About the role
Futures start here. Where first steps, new friendships, and confident learners are born. At KinderCare Learning Companies, the first and only early childhood education provider recognized with theGallup Exceptional Workplace Award, we offer a variety of early education and child care options for families. Whether it’s KinderCare Learning Centers, Champions, or Crème de la Crème, we build confidence for kids, families, and the future we share. And we want you to join us in shaping it—in neighborhoods, at work, and in schools nationwide.
At KinderCare Learning Companies, you’ll use your skills and expertise to support the work (and fun) that happens in our sites and centers every day. From marketers and strategists to financial analysts and data engineers, and so much more, we’re all passionate about crafting a world where children, families, and organizations can thrive.
As a Senior Machine Learning Engineer, you will apply your deep expertise in the Databricks Lakehouse Platform to develop, build, and operationalize scalable, production-grade predictive modeling applications within a modern enterprise data ecosystem.
You will lead end-to-end ML workflows in Databricks—including feature engineering, model training, deployment, monitoring, and optimization—working with tools like Delta Lake, MLflow tracking system, and feature management services, AutoML, Model Serving, along with Unity Catalog capabilities.
This role combines ML Engineering, Applied Data Science, and Platform Enablement, with a focus on building governed, adaptable ML platforms that speed up the deployment of AI technologies within enterprise environments. You will partner with Data Engineering, Analytics, and Product teams to deliver scalable AI solutions, establish ML standard processes, and help define the organization’s ML engineering standards.
Responsibilities:
- Databricks-Native ML Development: Design, develop, and deploy machine learning solutions using Databricks technologies including PySpark, Spark SQL, MLflow, Feature Store, AutoML, and notebooks to standardize experimentation and feature reuse.
- End-to-End ML Pipeline Architecture: Build scalable ML pipelines across the full lifecycle—from data ingestion and feature engineering to model validation, deployment, monitoring, and retraining within the Lakehouse platform.