Machine Learning Engineer

Arlo is rebuilding health insurance for small businesses by leveraging AI to improve efficiency. They are seeking a Machine Learning Engineer to build and own the infrastructure for their underwriting model, ensuring reliability and scalability while also facilitating data science iteration.

Responsibilities:

• Build and own the infrastructure layer that powers our underwriting model, trained on tens of millions of patients and hundreds of millions of rows of claims data
• Make training reliable, reproducible, and scalable as data volume and model complexity grow
• Build and own the API layer that produces quotes in seconds — serving a trained model against a much larger inference-time dataset, on the order of trillions of rows of claims across hundreds of millions of people
• Own the latency, reliability, and scalability of the serving path the quoting product depends on
• Make it as easy as possible for data scientists and actuaries to test new features and ideas
• Build backtesting and validation infrastructure so model performance can be measured quickly and trustworthily
• Remove friction from the path between an idea and a validated, production-ready model — make experimentation simpler than it's ever been