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Salary: USD 180,000 - 230,000 / annual
Arlo is rebuilding health insurance for small businesses by redirecting premium dollars toward care rather than administrative overhead. The company uses AI across underwriting, operations, clinical programs, and member experience to build a more efficient insurance model. Operating at meaningful scale with hundreds of millions in premiums and tens of thousands of members, Arlo is backed by top-tier VCs including Upfront Ventures, 8VC, and General Catalyst, with a team drawn from Palantir, YC companies, and healthcare operators.
As a Machine Learning Engineer, you'll own the ML infrastructure powering Arlo's core underwriting business. This role bridges infrastructure and data science, requiring you to build systems that handle massive scale while maintaining the ability to contribute directly to modeling work.
Key responsibilities include: (1) Building and owning training infrastructure for underwriting models trained on tens of millions of patient records and hundreds of millions of claims rows, ensuring reliability, reproducibility, and scalability as data volume and model complexity grow. (2) Owning the real-time inference API layer that produces insurance quotes in seconds, serving trained models against inference-time datasets on the order of trillions of rows across hundreds of millions of people, with accountability for latency, reliability, and scalability. (3) Accelerating data science iteration by building backtesting and validation infrastructure, removing friction from idea to production-ready model, and making experimentation simpler for data scientists and actuaries.
Required qualifications: Strong production track record building ML or data infrastructure at scale; deep Python proficiency with experience processing large datasets (Spark, Databricks, or equivalent); experience with model training pipelines and/or low-latency model serving in production; experience building developer/researcher-facing infrastructure tools (feature testing, experiment tracking, backtesting); ability to own systems end-to-end with production reliability standards (SLAs, monitoring, on-call); genuine interest in modeling work, not just infrastructure.
Nice-to-have: Prior experience in regulated spaces like healthcare or insurance; MLOps tooling experience (MLflow); feature stores or experimentation platforms; supporting data science or actuarial teams in production.
