Machine Learning Data Engineer (Contract)
Remote
Job Summary
Own tracking and reporting of computer vision accuracy metrics per customer and identifier type, investigating misclassifications to categorize root causes and identify patterns. Curate, label, and prioritize datasets for model retraining while building and improving the continuous learning pipeline to enable weekly model shipments. Define functional acceptance criteria for CV accuracy and translate findings into actionable decisions for engineering and stakeholders. As the pipeline matures, shift from flagging issues to directly fixing them by building labeling tooling, running retraining jobs, and owning error pattern resolutions. This role sits within a small, high-conviction team shipping real software to logistics operators, with revenue set to grow 10X over the next 18 months.
Required Qualifications
- 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
Desired Qualifications
- 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
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