Most of what makes American healthcare expensive isn’t medical care. It’s the machinery wrapped around it: middlemen taking a cut, fraud nobody stops, and billing systems designed to fight over payment instead of deliver care. The result is higher premiums, denied claims, surprise bills, and a system patients increasingly experience as adversarial.

Arlo is rebuilding health insurance for small businesses from first principles: making sure as much of every premium dollar as possible goes to care instead of getting absorbed by the system around it. We do that by identifying fraud earlier, steering members toward higher-quality and lower-cost care, automating operational overhead, and eliminating vendors whose business exists mostly to take a cut.

AI is the foundation that makes this work. We use it across underwriting, operations, clinical programs, and member experience to build an insurer that becomes more efficient as the technology improves.

We’re already operating at meaningful scale: profitable, hundreds of millions in premiums, tens of thousands of members covered, and growing quickly through brokers, employers, and partners. Backed by Upfront Ventures, 8VC, and General Catalyst, with a team from Palantir, YC companies, and longtime healthcare operators.

About the Role

Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale. We're hiring a Senior ML Engineer to build and own the infrastructure that powers it — from training models on tens of millions of patients and hundreds of millions of rows of claims data, to serving real-time quotes in seconds against inference-time datasets that run into the trillions of rows. You'll also build the tooling that lets our data scientists and actuaries iterate faster than ever.

This is an ML role with real room to do ML and data science. You'll own the platform, but you'll also have the opportunity to work alongside our data scientists and actuaries to test and evaluate your own ideas — not just support theirs.

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.