Online Annotation Jobs in California (NOW HIRING)

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 Perception Algorithm team transforms raw multimodal sensor data into high-quality robot training annotations. You will be deeply involved in the complete loop from data collection to model delivery - sensor calibration, SLAM localization, human pose estimation, perception model training, and embedded deployment. Your work directly determines the quality ceiling of our training data.

• 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

• 5+ years of industry experience in robot perception or computer vision
• Strong 3D vision fundamentals: stereo and structured-light camera principles, 3D reconstruction
• Proficiency with SLAM frameworks (ORB-SLAM, VINS-Mono, FastLIO, etc.) or V-SLAM system development experience
• Hands-on engineering experience with human pose estimation: hand joints (MediaPipe, MANO) or full-body pose (OpenPose, SMPLify, etc.)
• Proficient in deep learning training frameworks for perception model training, tuning, and evaluation
• TensorRT deployment experience with real-time inference optimization on embedded platforms (Jetson, Horizon, etc.)
• CUDA programming fundamentals; ability to write or debug custom kernels
• Proficient in C++ and Python with ROS / ROS2 development experience
• Proficient with AI coding agents

• Engineering experience with 6DoF object pose estimation (FoundPose, FoundationPose, GDR-Net, etc.)
• Familiarity with 3D Gaussian Splatting or NeRF for scene reconstruction or data augmentation
• Experience with robot manipulation or teleoperation systems
• End-to-end development experience with automated annotation pipelines or ground truth generation systems
• Published research in perception, pose estimation, or robotics

• Direct involvement in the most critical technical challenge in embodied intelligence: producing high-quality robot training data
• An environment working alongside top-tier robotics engineers and ML researchers
• Proprietary hardware platforms (humanoid robots, camera arrays, data gloves)
• A fast-paced, high-autonomy 0→1 work environment