Senior ML Engineer — Real-Time Underwriting Platform

Arlo; New York (NY); On-site; USD 180,000 - 230,000; Full time

Job summary

Arlo is hiring a Senior ML Engineer to own the underwriting infrastructure, training pipelines, and real-time quoting workloads. You will scale models from tens of millions of patients to trillions of claim rows, and partner with data scientists and actuaries to ship production-ready features.

You’ll own end-to-end systems, build reliable pipelines, and drive low-latency serving while expanding tooling for experimentation. Join a mission to apply AI to healthcare at scale.

Qualifications

• 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.

Responsibilities

• 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.

Skills

ML infrastructure

Python

Large datasets

Model training pipelines

Low-latency serving

Tooling for faster experimentation

End-to-end ownership

Tools

Spark

Databricks

Job description

Arlo is hiring a Senior ML Engineer to own the underwriting infrastructure, training pipelines, and real-time quoting workloads. You will scale models from tens of millions of patients to trillions of claim rows, and partner with data scientists and actuaries to ship production-ready features.

You’ll own end-to-end systems, build reliable pipelines, and drive low-latency serving while expanding tooling for experimentation. Join a mission to apply AI to healthcare at scale.