Senior Data Quality Engineer, Data Quality Services

We are seeking a Senior Data Quality Engineer to define, measure, and automate the quality of the training and validation data used for robotics, autonomy, computer vision, and Vision-Language-Action (VLA) systems with a scrum team.

This role combines data engineering, machine learning, and quality engineering to build the quality gates, metrics, and validation tooling that ensure every dataset—from raw camera and LiDAR captures to auto-labeled annotations and captions—meets the accuracy, consistency, and coverage standards required to train and evaluate Caterpillar autonomy models. You will partner closely with AI Automation, Data Engineering, perception/ML model teams, and external annotation providers, leveraging technologies such as NVIDIA Cosmos Curator and Cosmos Reason alongside Caterpillar-specific AI models to scale AI-driven quality assurance.

This is a hands-on technical role focused on making data quality measurable, automated, and continuously improving at production scale.

• ◆Define data quality standards, acceptance criteria, and annotation guidelines for sensor data, labels, and captions used in perception and VLA datasets.
• ◆Build automated quality gates that validate data at every stage, from ingestion and labeling through dataset publication.
• ◆Develop and deploy AI-driven quality checks such as label-noise detection, consensus scoring, and caption verification using vision-language models (e.g., Cosmos Reason).
• ◆Define and track dataset quality KPIs such as label accuracy, inter-annotator agreement, and rework rate, and share results through dashboards and reports.
• ◆Audit internal and vendor annotations using gold datasets and statistical sampling, and manage annotation providers through scorecards, feedback loops, and SLAs.
• ◆Analyze dataset coverage and gaps, trace model failures back to data issues, and ensure dataset lineage, versioning, and governance.

What Makes This Role Unique

Most data quality practices were built for structured business data—not for multimodal sensor data captured from heavy equipment operating on dynamic jobsites.

In this role, you'll define what "good data" means for Caterpillar's autonomy and Physical AI programs. You'll work across computer vision, LiDAR, multimodal AI, and Vision-Language-Action datasets, directly influencing model performance and machine safety by ensuring the data that trains intelligent machines is accurate, consistent, and representative of real-world operating conditions.

Basic

• ✓Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, Robotics, or a related field.
• ✓3+ years of experience developing production-grade software using Python and SQL, modern software engineering practices, and containerization platforms (e.g. Docker, Kubernetes).
• ✓3+ years of experience designing and implementing data pipelines, APIs, and distributed data processing systems.
• ✓3+ years of experience building data validation frameworks and automated quality checks for large-scale datasets.
• ✓Strong understanding of statistics and sampling methods used to audit and measure data quality.
• ✓3+ years of experience managing Camera and LiDAR sensor datasets for autonomy or computer vision, including ground truth generation and annotation quality assurance.
• ✓Demonstrated ability to define annotation guidelines, identify labeling defects, and drive measurable improvements in dataset accuracy.
• ✓3+ years of experience using annotation and dataset management tools (e.g. Labelbox, CVAT, FiftyOne) and machine l