Data & ML Engineer

Role overview This role builds the data and machine learning infrastructure that powers intelligent routing, safety insights, and operational analytics for a transportation and fleet platform. The work spans data pipelines, schemas, deployed models, and monitoring, turning raw telematics and fleet data into insights that improve non-emergency medical transport and other fleet operations. It suits an engineer who can collaborate with product and operations teams to identify high-impact machine learning opportunities.

Responsibilities - Design and build data pipelines to ingest, transform, and store telematics and fleet data - Develop machine learning models for route optimization, ETA prediction, and safety scoring - Create data schemas and warehouse structures that support analytics and reporting needs - Deploy and monitor ML models in production, including A/B testing and ongoing evaluation - Partner with product and operations to surface high-impact ML use cases

Requirements - Three or more years of experience in data engineering or ML engineering - Strong proficiency in Python and SQL - Experience building data pipelines using tools such as Airflow or dbt - Familiarity with ML frameworks such as scikit-learn, TensorFlow, or PyTorch - Understanding of feature engineering, model evaluation, and deployment

Nice to have - Experience with geospatial data and routing or logistics problems - Background in time-series forecasting or anomaly detection - Familiarity with real-time ML inference and streaming data - Previous work in transportation, logistics, or fleet management

Benefits and work setup - Remote, full-time position on the Math & ML team