ML Data Engineer

About the Company

Outpost is building the backbone of freight by reinventing supply chain infrastructure with carrier agnostic truck terminals. As a vertically integrated real estate, operations, and technology company, Outpost acquires and operates mission-critical real estate to serve the largest logistics providers in the world.

Responsibilities

• Own tracking and reporting of CV accuracy metrics per customer and identifier type
• Investigate misclassifications and false negatives to identify root causes and patterns
• Curate, label, and prioritize datasets for model retraining in partnership with ML and CV engineers
• Build and improve continuous learning pipelines to enable weekly model shipments
• Define functional acceptance criteria for CV accuracy and track progress
• Translate accuracy findings into actionable decisions for engineering and stakeholders
• Build labeling/preprocessing tooling and own fixes for error patterns as the pipeline matures

Requirements

• 3+ years in data quality, ML data engineering, or applied ML roles
• Experience with computer vision or object detection systems in production
• Proficiency in Python for data analysis, pipeline automation, and dataset tooling
• Strong analytical rigor for digging into large volumes of imagery and data
• Experience with dataset annotation/labeling tools (Roboflow, Labelbox, CVAT, etc.)
• Strong written English communication skills for asynchronous engagement

Preferred Qualifications

• Experience with continuous learning or active learning pipelines
• Familiarity with OCR systems and identifier recognition
• Experience partnering with customer success or support teams
• Background in QA/test engineering for ML systems
• Specific experience with Roboflow