Robotics Data Quality Engineer

What You’ll Do

Data quality engineers are the bridge between raw collection and usable training data. Sample projects include:

• analyzing robotics data across modalities to identify quality issues: plotting joint velocities, validating camera poses, checking gripper encoder accuracy, and flagging anomalous collection sessions
• building automated validation pipelines that run on ingestion and catch problems before data enters the warehouse
• designing and documenting data formats and schemas across collection modalities, ensuring they are consistent, versioned, and well-understood by partners and internal researchers
• building data visualization tools and dashboards so the broader team can inspect and understand the data without writing custom scripts
• validating cross-modal temporal alignment, including timestamp synchronization, dropped frame detection, and clock drift across camera, joint, and gripper streams
• defining quality metrics and thresholds per modality and tracking whether data quality is improving or degrading as collection scales
• cataloging edge cases and failure modes into a shared taxonomy so the team has a common language for data issues
• working closely with data collection operators to trace quality issues back to their root cause, whether systemic (hardware calibration, sensor drift) or operator-specific

About You

Baseline skills:

• bachelor’s or master’s degree (or equivalent experience) in robotics, computer science, mechanical engineering, or a related field
• strong Python data skills (numpy, pandas, matplotlib or plotly) and comfort working with large, messy datasets
• solid understanding of 3D geometry, coordinate frames, and spatial transformations
• intuition for physical systems: you can look at a trajectory or a joint velocity plot and tell when something is off
• experience designing or working with structured data formats (protobuf, HDF5, ROS bags, or similar)

You might be a good fit if you:

• have hands-on experience with robotics data, whether from a research lab, a robotics startup, or a manipulation/locomotion project
• have worked with teleoperation systems, motion capture, or egocentric data collection
• have experience with signal processing, sensor fusion, or time-series analysis
• have built internal data visualization tools or dashboards for technical teams
• have worked on data versioning, lineage tracking, or schema migration in a production setting
• are very comfortable working in 0→1 environments
• are mission-driven and passionate about robotics: work at xdof is fast-paced and constant. We hope you love what you’re going to be doing, because you’ll be doing a lot of it!