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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning Data Engineer (Contract) - **Company:** The Outpost - **Location:** United States (Remote available) - **Experience:** Starter - **Salary:** $43,000.0 - $58,000.0 - **Contract:** Temporary to permanent - **Skills:** Artificial Intelligence, Computer Vision, Big Data, Information Engineering, Data Files, Python (Programming Language), PostgreSQL, Microsoft Office, Node.Js, Object Detection, TypeScript, Delivery Pipeline, Snowflake, Information Technology, Machine Learning Operations - **Published:** August 18, 2026 - **Apply:** https://jobs.ashbyhq.com/Outpost/0653bc36-22ee-4922-aab7-5fafdf589443 ## 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 computer-vision accuracy end-to-end: measure per-customer metrics, investigate misclassifications, curate and label datasets, prioritize retraining, and build continuous learning pipelines. Partner with ML/CV engineers and stakeholders to define acceptance criteria, translate findings into actionable fixes, and eventually implement tooling and retraining to ship regular model improvements. 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, and yards., This role is a full-time contract position. You'll work closely with our core engineering team - embedded in our sprints, standups, and Slack channels - but employment is managed through the agency. We've built this model successfully with engineers in Latin America, and it's been a great fit for both sides., Handle incoming owner relations inquiries about revenue, land, division orders, JIB, A/R, and A&P. Log and follow up cases in a tracking system, build trusting client relationships, manage difficult situations calmly, provide accurate timely responses, and participate in cross-training. 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