Defining motion for autonomous systems. Backed by a16z.
About the role
Robotics data 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 Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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)
- Have hands-on experience with robotics data, whether from a research lab, a robotics startup, or a manipulation/locomotion project
More about this role
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 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.
Robotics data 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...
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