Note: The job is a remote job and is open to candidates in USA. Outpost is building logistics infrastructure supported by AI-powered gate automation, computer vision, and operational software. The Machine Learning Data Engineer will own computer vision accuracy measurement, investigate model errors, curate and label retraining data, and improve continuous learning pipelines. The role will increasingly contribute directly to tooling, retraining jobs, and production fixes.
Responsibilities
- 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
Skills
- • 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
- Remote — Latin America preferred
Benefits
- Remote work arrangement — Latin America preferred
- Full-time contract position
- Embedded in core engineering team sprints, standups, and Slack channels
Company Overview