Founding Software Engineer - Data Platform

Arlo is rebuilding health insurance from the ground up using AI. The healthcare experience today is expensive, confusing, and often so frustrating that people delay the care they need. We’re changing that by reimagining what a health plan should be: a proactive partner that enables health rather than denying it. Our AI-native platform delivers continuous, personalized support for members—helping them navigate benefits, schedule appointments, access high-quality care, and avoid financial fear.

Powered by the industry’s most advanced risk-pricing engine, Arlo is already scaling fast: we’ve grown to $XXXM in premiums, cover tens of thousands of people, and see accelerating demand across brokers, employers, and partners. Backed by Upfront Ventures, 8VC, and General Catalyst, our team combines deep industry expertise (Palantir, YC) with the ambition to modernize a $1T market.

As the Founding Software Engineer on our Data Platform team at Arlo, you will build the foundation for our ML-driven health insurance quoting system. You'll design and implement the entire data infrastructure that powers our core product, working at the intersection of data engineering and machine learning operations.

• Design and build scalable data pipelines that process billions of healthcare claims records
• Develop robust ETL workflows to ingest, normalize, and transform medical data from multiple sources
• Create infrastructure for real-time ML inference serving hundreds of millions of predictions
• Build developer tooling to accelerate the work of our data scientists and ML engineers
• Architect and implement monitoring systems to ensure data quality and model performance
• Optimize PySpark code for feature construction to improve performance and efficiency

Healthcare Data Platform:

• Build a unified data platform that ingests medical data from diverse sources and normalizes it for both training and inference
• Develop workflow orchestration tools to automate complex data processing pipelines
• Create CI/CD pipelines for reliable deployment of data transformations and ML models
• Refactor and optimize existing PySpark code to enhance performance and scalability

ML Production Infrastructure:

• Design and implement real-time inference systems that can handle high-volume prediction requests
• Build infrastructure for A/B testing and controlled model rollouts
• Develop monitoring tools to track model performance and data drift in production

This role combines data engineering and ML infrastructure expertise to create the backbone of our AI-powered health insurance platform. You'll work across the full ML lifecycle, from initial data processing to production deployment and monitoring.

• 3+ years of experience as a backend engineer, data engineer, or software engineer with exposure to data pipelines and large-scale infrastructure.
• Strong understanding of data structures, algorithms, and scalable system design.
• Demonstrated ability to design secure, robust, and scalable infrastructure for data and backend systems.
• Hands‑on experience with big data tools (e.g., Spark) and pipeline orchestration platforms (e.g., Dagster, Airflow, Prefect).
• Proficiency in a backend programming language like Python and solid SQL skills for querying and database design.
• Familiarity with cloud platforms (AWS, GCP, or Azure) and their data services.

This is what you can expect when we like your application:

$200,000 – $220,000 + performance bonus + equity

• High ownership:
  You’ll get real responsibility from day one—our high‑trust team empowers you to run with big problems and shape core parts of the company.
• Join an important mission:
  Your work directly influences how people access care and improves lives at scale.
• Growth & expansion:
  We’re moving fast, and as we grow, your scope will grow with us—new…