Member of Technical Staff, Perception

XDOF is a company focused on building foundational data collection systems and software toolchains to advance general-purpose robotics. The Member of Technical Staff, Perception role involves transforming multimodal sensor data into high-quality robot training annotations, encompassing tasks from sensor calibration and SLAM localization to perception model training and embedded deployment. The position directly impacts the quality of training data essential for robotics development.

Responsibilities:

• Design and optimize hand pose estimation pipelines supporting accurate joint angle extraction from teleoperation data collection
• Build full-body pose estimation systems for motion capture and teleoperation action annotation ground truth generation
• Research and apply vision-based pose estimation methods (markerless) to reduce data collection costs
• Fuse pose estimation outputs with robot joint angle data to generate consistent training annotations
• Design and maintain intrinsic/extrinsic calibration pipelines for multi-camera arrays (factory calibration + online recalibration)
• Build visual SLAM / V-SLAM systems supporting real-time localization and scene reconstruction on data collection platforms
• Implement hand-eye calibration between cameras and robot end-effectors
• Develop temporal alignment solutions across multimodal sensors (cameras, IMU, data gloves, force sensors)
• Train and iterate on perception models including object detection, instance segmentation, and 6DoF pose estimation
• Optimize model inference using TensorRT / CUDA for real-time performance on robot embedded platforms
• Write custom CUDA kernels for low-level acceleration of perception tasks
• Design evaluation metric frameworks for perception models; continuously track the relationship between model performance and data quality
• Contribute to the design of automated annotation pipelines that convert sensor data into structured training labels
• Build Auto QA modules to filter low-quality data including anomalous frames, failed demonstrations, and sensor dropouts
• Collaborate with ML engineers and data infrastructure teams to ensure perception output formats meet downstream VLA model training requirements
• Establish feedback mechanisms linking perception accuracy to model training outcomes, continuously improving annotation quality