> Markdown version of [/jobs/ext/156259-senior-ml-infrastructure-engineer](https://www.wearedevelopers.com/jobs/ext/156259-senior-ml-infrastructure-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Senior ML Infrastructure Engineer - **Company:** Arlo Technologies, Inc. - **Location:** New York, NY, United States - **Experience:** Expert - **Salary:** $180,000.0 - $230,000.0 - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Application Programming Interfaces (APIs), Amazon Web Services, Business Logic, Software Quality, Data Infrastructure, Data Systems, Python (Programming Language), SQL Databases, Data Processing, Data Ingestion, Apache Spark, Git, Pyspark, Core Data, Production Code, Machine Learning Operations, Databricks - **Published:** May 30, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=1c5c28a9b3f8a39f ## About the Role Do you have experience in Technology infrastructure engineering?, This role is a great fit if you are an engineer who takes pride in writing high-quality, maintainable code and is energized by owning data systems end-to-end. * A strong track record of building scalable data systems and pipelines in production, with deep proficiency in Spark, Databricks, and modern data processing infrastructure (AWS or equivalent). * Fluency across our stack: SQL, PySpark, Python, and Git. You write maintainable code and hold a high bar for code quality on your team. * The ability to own workstreams end-to-end with minimal oversight. You make sound judgment calls independently, flag the right risks, and make the people around you better through standards, reviews, and process improvements. * Strong communication skills and a highly analytical mindset. You can work with non-technical partners - actuaries, ops, clinical - to turn requirements into action, and you test and analyze your own work before it ships. Nice to Haves * Prior experience in a regulated space like healthcare or insurance. * Experience supporting data science or actuarial teams in production environments. ## Description As a Senior ML Infrastructure Engineer, you will own the data infrastructure that powers our underwriting, claims, and operational workflows, translating complex business logic into reliable, scalable pipelines that the entire company depends on. * Own our data infrastructure and the core data platform pipelines that drive underwriting and claims. * Partner with data scientists and actuaries to turn business logic into production code, owning the underwriting pipeline end-to-end. * Set the standards for how we build, monitor, and operate data systems - establishing SLAs, on-call practices, and monitoring that the team relies on., To give you a sense of the impact you'll have, here are some initial projects you could own: * Underwriting Pipeline & API: Own and improve the pipelines and API that sit at the core of Arlo's underwriting engine. You'll work directly with data scientists and actuaries to translate underwriting logic into production code and ensure the systems that run it are fast, observable, and reliable. * Claims Ingestion & Enrichment Logic: Build and maintain the ingestion pipelines that bring third-party claims data into our platform. You'll design systems that handle messy, real-world claims data at scale and make it usable for downstream analytics, underwriting, and operations. * Company-Wide Data Foundations: Architect and build the data infrastructure that powers operational workflows across the business - quoting portal, cost containment, member engagement - and extend it to non-underwriting teams, including sales ops, finance, and clinical. ## Related Videos - [Empowering Retail Through Applied Machine Learning](https://www.wearedevelopers.com/videos/976-empowering-retail-through-applied-machine-learning) - [From Messy Queries to Scalable Systems - How Data Engineering actually works](https://www.wearedevelopers.com/videos/100203-from-messy-queries-to-scalable-systems-how-data-engineering-actually-works) - [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data) - [How a Small Team Shrank a Microsoft Monorepo by 94%](https://www.wearedevelopers.com/videos/1236-how-a-small-team-shrank-a-microsoft-monorepo-by-94) - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [Building Multi-Tenant ASP.NET Core Applications: Best Practices and Real-World Solutions](https://www.wearedevelopers.com/videos/1552-building-multi-tenant-asp-net-core-applications-best-practices-and-real-world-solutions) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Dev Digest 120 - Apple and peers](https://www.wearedevelopers.com/magazine/455-dev-digest-120-apple-and-peers) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [Dev Digest 121 - AI goes offline](https://www.wearedevelopers.com/magazine/456-dev-digest-121-ai-goes-offline) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift)