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# Founding ML Researcher

- **Company:** THE HR PLUG LLC
- **Location:** San Francisco, CA, United States
- **Salary:** $200,000.0 - $300,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Machine Learning, Unstructured Data, Machine Learning Operations, Data Pipelines
- **Published:** July 7, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=e0109e1abe3dbbfe

## About the Role

This role is research-heavy but product-oriented. You should be comfortable moving between theory, experimentation, and real-world deployment. You should have experience with most of the following: Training and deploying state-of-the-art models for parsing and understanding unstructured data Experimenting with novel techniques to improve layout models and VLM-based document understanding Building data pipelines, evaluating model performance, and integrating models into production systems Working directly with the founders to shape the product direction and engineering strategy

Bonus if you have PhD or equivalent research experience in VLM, Computer Vision or related areas. Publications in top-tier AI conferences Familiarity with model serving, inference optimization, or deployment at scale

## Description

We are hiring a Founding ML Researcher in San Francisco. We are building a small, talent-dense team. This role will define the engineering archetype at Unsiloed AI and set the ceiling for the team. We strongly believe technical DNA compounds (or degrades) with every hire and hence the first few matter disproportionately. You will be expected to operate proactively, take full ownership, and independently drive systems from idea to production. What you Will Do As a Founding ML Researcher, you will shape the company's ML research direction and translate research into production-ready document AI models. Own the end-to-end ML lifecycle: research experimentation training evaluation production deployment Work with engineering team to transition research into deployed, scalable systems Drive best practices for data, experimentation, evaluation, and model iteration.


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