Senior ML Engineer

The role

You will build and own the infrastructure that powers underwriting models. This includes training infrastructure, real-time inference for quoting, and tools to accelerate data science iteration.

What you will do

• Build and own the infrastructure layer that powers the underwriting model.
• Make training reliable, reproducible, and scalable.
• Build and own the API layer that produces quotes in seconds.
• Own the latency, reliability, and scalability of the serving path.
• Make it easy for data scientists and actuaries to test new features and ideas.
• Build backtesting and validation infrastructure.
• Remove friction from the path between an idea and a production-ready model.

Required qualifications

• Strong track record building ML or data infrastructure in production at scale.
• Deep proficiency in Python.
• 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.
• Ability to own systems end-to-end, set standards, and operate reliable production infrastructure (SLAs, monitoring, on-call).
• Genuine interest in the modeling itself.

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.

Benefits

• High ownership
• Join an important mission
• Growth & expansion
• Apply AI to a problem that matters
• High pace, high collaboration

About the company

Arlo is rebuilding health insurance for small businesses from first principles. They use AI across underwriting, operations, clinical programs, and member experience.