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# Machine Learning Engineer

- **Company:** Yobi Corporation
- **Location:** New York, NY, United States (Remote available)
- **Salary:** $180,000.0 - $275,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Continuous Integration, Github, Machine Learning, Open Source Technology, Recommender Systems, Apache Spark, Build Tools, Data Pipelines
- **Published:** September 4, 2026
- **Apply:** https://startup.jobs/machine-learning-engineer-applied-product-yobi-6242613

## About the Role

* Understanding enough about machine learning to be dangerous but not necessarily published in the field. This means you have worked on and can speak to impactful consumer-facing ML problems, e.g. recommender systems, personalization, etc. that you have directly contributed to.
 * Skill and attitude wise, you can quickly contribute to the "full stack" of our pipeline. This includes things such as data orchestration, build systems, and experiment tracking. Although we use a combination of open source products like Airflow, Bazel, Github CI/CD, and Spark, prior experience with these specific solutions is not needed. However, a good part of your day to day will involve interacting with these systems, so you should be comfortable with getting your hands dirty.
 * Good product sense, has opinions on what we should and shouldn't be doing both in chasing product-market fit and on the implementation side.

## Description

At Yobi, Applications teams bring the value in our User-Behavioral foundation models to market, creating scalable, highly profitable products with ML at their heart. These Applications products are key to Yobi success, as they ground the value of our R&D, power continuous experimentation and improvement, and provide significant data for improving our core embeddings.

As an MLE on this team, you will primarily be focused on the models, metrics, pipelines, systems, and services that power and deliver excellence via Yobi Applications products.

This role involves a large degree of 0-to-1 development, and will rely on collaboration with Product, core signals MLEs, and leaning on your own expertise and insight in building holistic ML-powered products. While we currently have a product in the market here, we invite big bets to expand impact and reach.

Significant "wearing your Product hat" is expected, along with driving results in the many domains required to deliver whole ML-powered products - we are a quickly growing startup after all!


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