About the role
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 We’re looking for AI Forward Deployed Engineers to embed directly inside the core functions of our business and rebuild how work gets done. At most insurance companies, teams such as underwriting, claims, premium finance, member support, care navigation, and sales operations each rely on extensive ecosystems of external vendors and legacy SaaS tools. At Arlo, we build those systems ourselves. Your job is to work shoulder-to-shoulder with these teams, deeply understand how their operations function, and use AI to automate, augment, and redesign their workflows from the ground up. In practice, that means building AI agents, internal tooling, copilots, decision systems, and operational infrastructure that allow teams to scale far beyond what traditional headcount would support. This is not a pure research role, nor is it traditional software engineering. You’ll spend time inside the business: shadowing operators, identifying bottlenecks, mapping workflows, and rapidly deploying systems that eliminate manual work and compress operational overhead. The goal is simple: every team at Arlo should become AI-native. What You’ll Do Embed within operational teams across the company, underwriting, claims, premium finance, patient experience, broker operations, sales engineering, and more, and work directly with them to understand how decisions are made, where work slows down, and what can be automated. Build AI underwriting agents that synthesize risk, summarize cases, and assist decision-making. Automate claims workflows that currently require manual review and coordination. Create internal copilots for support and care navigation teams. Design systems that extract structure and actions from messy healthcare documentation. Deploy retrieval, memory, and evaluation systems that improve operational reliability over time. Replace repetitive back-office workflows with AI-native tooling and agents. You’ll move quickly, ship directly into production, and iterate closely with the teams using what you build. Prototypes or demos do not measure success in this role; it’s measured by operational leverage: faster workflows, fewer manual processes, lower costs, and teams that can scale far more efficiently because of the systems you deployed. What We’re Looking For You’re obsessed with customers; their success is your scoreboard. You don’t quit on outcomes. Mission accomplished means real, visible results, not just release notes. You use feedback as fuel, turning customer insights into new ideas and features overnight. You’ve shipped full-stack AI and LLM apps, and you know your way around Python, Postgres, and modern AI workflows. You handle tradeoffs: fast but safe, powerful but reliable. You build guardrails into everything you ship. You use AI-powered developer tools to move at startup speed. Target
