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
Skills
TensorRT
CUDA
C++
Python
ROS
ROS2
SLAM
ORB-SLAM
VINS-Mono
FastLIO
MediaPipe
MANO
OpenPose
SMPLify
FoundPose
FoundationPose
GDR-Net
NeRF
3D Gaussian Splatting
About the company
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.
About the role
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.
Core Responsibilities
Human Pose Estimation
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Design and optimize hand pose estimation pipelines supporting accurate joint angle extraction from teleoperation data collection
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Build full-body pose estimation systems for motion capture and teleoperation action annotation ground truth generation
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Research and apply vision-based pose estimation methods (markerless) to reduce data collection costs
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Fuse pose estimation outputs with robot joint angle data to generate consistent training annotations
Robot Perception & Calibration
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Design and maintain intrinsic/extrinsic calibration pipelines for multi-camera arrays (factory calibration + online recalibration)
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Build visual SLAM / V-SLAM systems supporting real-time localization and scene reconstruction on data collection platforms
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Implement hand-eye calibration between cameras and robot end-effectors
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Develop temporal alignment solutions across multimodal sensors (cameras, IMU, data gloves, force sensors)
Perception Model Training & Deployment
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Train and iterate on perception models including object detection, instance segmentation, and 6DoF pose estimation
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Optimize model inference using TensorRT / CUDA for real-time performance on robot embedded platforms
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Write custom CUDA kernels for low-level acceleration of perception tasks
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Design evaluation metric frameworks for perception models; continuously track the relationship between model performance and data quality
End-to-End Loop from Data Collection to Model Delivery
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Contribute to the design of automated annotation pipelines that convert sensor data into structured training labels
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Build Auto QA modules to filter low-quality data including anomalous frames, failed demonstrations, and sensor dropouts
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Collaborate with ML engineers and data infrastructure teams to ensure perception output formats meet downstream VLA model training requirements
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Establish feedback mechanisms linking perception accuracy to model training outcomes, continuously improving annotation quality
Requirements
Must-Have
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5+ years of industry experience in robot perception or computer vision
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Strong 3D vision fundamentals: stereo and structured-light camera principles, 3D reconstruction
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Proficiency with SLAM frameworks (ORB-SLAM, VINS-Mono, FastLIO, etc.) or V-SLAM system development experience
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Hands-on engineering experience with human pose estimation: hand joints (MediaPipe, MANO) or full-body pose (OpenPose, SMPLify, etc.)
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Proficient in deep learning training frameworks for perception model training, tuning, and evaluation
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TensorRT deployment experience with real-time inference optimization on embedded platforms (Jetson, Horizon, etc.)
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CUDA programming fundamentals; ability to write or debug custom kernels
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Proficient in C++ and Python with ROS / ROS2 development experience