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# ML Data Engineer

- **Company:** The Outpost
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Information Engineering, Data Files, Object Detection, Delivery Pipeline, Machine Learning Operations
- **Published:** August 1, 2026
- **Apply:** https://jobs.ashbyhq.com/Outpost/263e3525-81fd-4fca-8f30-5b69862df6aa

## About the Role

* 3+ years in a data quality, ML data engineering or applied ML role.
 * Experience working with computer vision or object detection systems in production.
 * Comfortable writing Python for data analysis, pipeline automation, and dataset tooling.
 * Strong analytical rigor, comfortable digging into large volumes of imagery/data to find patterns, not just running a script and reporting a number.
 * Experience with dataset annotation/labeling tools and workflows (Roboflow, Labelbox, CVAT, or similar).
 * Strong communication skills in English - you write clearly and engage well async., * Experience with continuous learning or active learning pipelines for production ML systems.
 * Familiarity with OCR systems and identifier recognition (plates, container numbers, etc.).
 * Experience partnering with customer success or support teams on quality metrics.
 * Background in QA/test engineering for ML systems.
 * Experience with Roboflow specifically.

## Description

Remote
Hiring Remotely in USA
Mid level
Remote
Hiring Remotely in USA
Mid level
Own CV accuracy end-to-end: measure and report metrics, investigate misclassifications, curate and label datasets, prioritize retraining, and build continuous learning pipelines. Translate findings into actionable fixes and acceptance criteria, collaborate with ML/CV engineers and customer teams, and eventually implement tooling and model retraining to reduce recurring error patterns in production.
The summary above was generated by AI, * Own tracking and reporting of CV accuracy metrics, per customer and per identifier type.

 * Investigate misclassifications and false negatives, categorize root causes, and identify patterns across customers and yards.
 * Curate, label, and prioritize datasets for model retraining, partnering closely with our ML and CV engineers.
 * Build and improve the continuous learning pipeline so new models ship weekly with minimal manual engineering effort.
 * Define functional acceptance criteria for CV accuracy per customer and track progress against them.
 * Translate accuracy findings into decisions the engineering team and customer-facing stakeholders can act on.
 * As the pipeline matures, expect to move from flagging issues to fixing them directly; building the labeling/preprocessing tooling, running retraining jobs, and owning fixes for the error patterns you find, not just reporting them.

What You Can Expect:

 * Direct ownership over the metric that decides whether our product works in the real world.
 * A small team that moves fast, argues in good faith, and trusts engineers to make decisions.
 * Real influence on what the ML team builds next; your findings drive the roadmap, not the other way around.
 * Problems grounded in the physical world: gates, cameras, trucks, yards.


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