Principal Software Engineer, Dataset and Annotation Quality

What you'll do

• Design the end-to-end system architecture for transforming raw sensor data into high-quality labeled datasets.
• Define data quality frameworks, including validation rules, scoring models, and acceptance criteria for ML systems.
• Establish engineering standards for label consistency, ontology design, and anomaly detection at scale.
• Develop systems for active learning, error mining, and continuous data quality monitoring based on model behavior.
• Provide technical leadership and design direction to annotation and dataset quality engineering teams.
• Lead hands-on development and prototyping using Python or Java in enterprise-scale cloud environments.
• Champion SDLC disciplines including CI/CD, automated testing, and infrastructure as code.
• Translate business objectives into actionable technical roadmaps and lead design reviews across multiple agile teams.

What we're looking for

• This position requires the candidate to be based in Irving, TX (Dallas).
• Bachelor’s or Master’s degree in Computer Science, Software Engineering, Computer Engineering, or a related field (preferred).
• 10+ years of software engineering experience with significant experience in principal, staff, or lead-level technical leadership roles (preferred).
• Expert-level proficiency in Python and/or Java (preferred).
• Expertise in computer vision, sensor-based systems, or ML-driven products (preferred).
• Strong experience designing annotation platforms, labeling systems, or data quality frameworks (preferred).
• Deep expertise in system design, distributed systems, and large-scale cloud-native architectures (preferred).
• Experience supporting AI/ML, autonomous systems, computer vision, robotics, or advanced analytics platforms (preferred).