Machine Learning Engineer

📋 Description

• Build and own underwriting ML infrastructure.
• Real-time quotes API layer for low-latency serving.
• Train models on tens of millions of patients and hundreds of millions of claims.
• Ensure training is reliable, reproducible, scalable as data grows.
• Enable data scientists and actuaries to test ideas quickly.
• Own end-to-end production infrastructure with SLAs and monitoring.

🎯 Requirements

• Strong track record building ML/data infra in production at scale.
• Proficiency in Python; comfortable with large datasets (Spark/Databricks).
• Experience with model training pipelines and low-latency serving.
• Experience building tooling that makes others faster (experimentation, backtesting).
• Ability to own end-to-end systems with SLAs, monitoring, on-call.
• Genuine interest in modeling and hands-on data work.

🎁 Benefits

• High ownership from day one.
• Impact on healthcare access and lives at scale.
• Growth, expansion, and fast career velocity.
• Apply AI to a problem that matters in healthcare.
• High pace, collaborative team environment.