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Arlo

Machine Learning Engineer

New York CityfulltimemidAdded today

About this role

Arlo seeks an ML Engineer to build and own the infrastructure powering their AI-driven health insurance underwriting platform. You'll design training pipelines for massive healthcare datasets, real-time inference serving, and tooling that accelerates data science iteration—while staying hands-on with modeling itself.

What you'll do

  • Build and maintain training infrastructure for underwriting models using tens of millions of patient records and hundreds of millions of claims rows
  • Design and own real-time inference APIs that serve quotes in seconds against trillion-row datasets
  • Create backtesting and validation infrastructure for rapid model experimentation and performance measurement
  • Develop tooling that enables data scientists and actuaries to test features and iterate faster
  • Ensure reliability, latency, and scalability of production ML serving infrastructure with defined SLAs
  • Collaborate with data scientists on modeling ideas while maintaining platform ownership

What they're looking for

  • Python (production-level proficiency)
  • Large-scale data processing (Spark, Databricks, or equivalent)
  • Model training pipelines and infrastructure
  • Low-latency model serving and inference optimization
  • Production systems design and operational excellence (monitoring, SLAs, on-call)
  • ML infrastructure and tooling development
  • Feature engineering and experimentation frameworks
  • Healthcare or regulated industry domain knowledge (nice-to-have)
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Arlo

Arlo is an AI-driven health insurance company using artificial intelligence to reduce healthcare costs and improve operations. The company is hiring AI engineers, data engineers, and product engineers to build AI agents, data pipelines, and member-facing features that transform underwriting, claims, member support, and pricing across their platform.

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Likely interview questions

  • Describe a large-scale ML training pipeline you've built—what were the bottlenecks and how did you solve them?
  • How would you design a real-time inference service that needs to serve predictions in milliseconds against a trillion-row dataset?
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