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# Machine Learning Engineer

- **Company:** Arlo Technologies, Inc.
- **Location:** New York, NY, United States
- **Salary:** $180,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Big Data, Data Infrastructure, Python (Programming Language), Machine Learning, Apache Spark, Low Latency, Machine Learning Operations, Databricks
- **Published:** July 31, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=ee75a378aa058ee8

## About the Role

A strong track record building ML or data infrastructure in production at scale.

 * Deep proficiency in Python, with comfort in processing large datasets (Spark, Databricks, or equivalent).
 * Experience with model training pipelines and/or low-latency model serving in production.
 * Experience building tooling that makes other people faster - feature testing, experiment tracking, backtesting, or similar developer/researcher-facing infrastructure.
 * The ability to own systems end-to-end, set standards, and operate reliable production infrastructure (SLAs, monitoring, on-call).
 * Genuine interest in the modeling itself - you want to occasionally get your hands into the data science, not only the infrastructure., Prior experience in a regulated space like healthcare or insurance.
 * Experience with MLOps tooling (MLflow or similar), feature stores, or experimentation platforms.
 * Experience supporting data science or actuarial teams in production environments.

## Description

Build and own the API layer that produces quotes in seconds - serving a trained model against a much larger inference-time dataset, on the order of trillions of rows of claims across hundreds of millions of people.

 * Own the latency, reliability, and scalability of the serving path the quoting product depends on.

Accelerate data science iteration
*
Make it as easy as possible for data scientists and actuaries to test new features and ideas.

 * Build backtesting and validation infrastructure so model performance can be measured quickly and trustworthily.
 * Remove friction from the path between an idea and a validated, production-ready model - make experimentation simpler than it's ever been.


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- [Swapping Low Latency Data Storage Under High Load](https://www.wearedevelopers.com/videos/746-swapping-low-latency-data-storage-under-high-load)
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- [PySpark - Combining Machine Learning & Big Data](https://www.wearedevelopers.com/videos/44-pyspark-combining-machine-learning-big-data)

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