Machine Learning Engineer

What You’ll Work On
Training infrastructure for underwriting

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.
Real-time inference for quoting

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.
Accelerate data science iteration

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.

What We’re Looking For
A strong track record building ML or data infrastructure in production at scale.
Deep proficiency in Python, with comfort in processing large datasets (Spark, Databricks, or equivalent).
Experience with model training pipelines and/or low-latency model serving in production.
Experience building tooling that makes other people faster — feature testing, experiment tracking, backtesting, or similar developer/researcher-facing infrastructure.
The ability to own systems end-to-end, set standards, and operate reliable production infrastructure (SLAs, monitoring, on-call).
Genuine interest in the modeling itself — you want to occasionally get your hands into the data science, not only the infrastructure.

Nice to Have
Prior experience in a regulated space like healthcare or insurance.
Experience with MLOps tooling (MLflow or similar), feature stores, or experimentation platforms.
Experience supporting data science or actuarial teams in production environments.