Robotics Data Quality Engineer

At xdof, we’re at an inflection point. Frontier labs are racing to build general-purpose robots, and high-quality training data is the bottleneck. We’re building the foundation behind the foundation models – the data collection systems, operational capability, exabyte-scale data warehouse, and software toolchain – to help our partners drive the field forward.

The models are only as good as the data. We’re looking for a Robotics Data Quality Engineer to be the person who knows whether our data is trustworthy, across every modality we collect. You’ll analyze, validate, and build tooling around data from teleoperation on real hardware, egocentric capture, UMI-style grippers, and more. If something is wrong with the data, you’re the first to catch it and the one who helps us fix the process.

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!