Machine Learning Engineer - arlo

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.

Arlo's underwriting is the core of the business, and it runs on machine learning at serious scale. We're hiring 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 infrastructure 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.