Machine Learning Engineer

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. 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.

Responsibilities

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; 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; 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

Requirements

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

Benefits

Bonuses; Equity; High ownership; Important mission; Growth & expansion; Apply AI to a problem that matters; High pace, high collaboration