4 months ago
Responsibilities
- Design and optimize hand and full-body human pose estimation systems for teleoperation and motion-capture annotation.
- Develop camera calibration, hand-eye calibration, visual SLAM, scene reconstruction, and temporal sensor-alignment pipelines.
- Train and evaluate object detection, instance segmentation, and 6DoF pose-estimation models.
- Optimize real-time perception inference with TensorRT and CUDA on embedded robot platforms, including custom CUDA kernels.
- Build automated annotation and Auto QA pipelines that convert sensor data into training labels and filter low-quality data.
- Collaborate with ML engineering and data infrastructure teams to connect perception outputs with downstream VLA model training.
- Establish feedback mechanisms linking perception accuracy, data quality, and model-training outcomes.
Requirements
- 5+ years of industry experience in robot perception or computer vision.
- Strong 3D vision fundamentals, including stereo cameras, structured-light cameras, and 3D reconstruction.
- Proficiency with SLAM frameworks such as ORB-SLAM, VINS-Mono, and FastLIO, or equivalent V-SLAM development experience.
- Hands-on human pose-estimation experience using hand-joint or full-body pose methods such as MediaPipe, MANO, OpenPose, or SMPLify.
- Proficiency with deep learning training frameworks for perception model training, tuning, and evaluation.
- Experience deploying TensorRT models and optimizing real-time inference on embedded platforms such as Jetson or Horizon.
- CUDA programming fundamentals and ability to write or debug custom kernels.
- Proficiency in C++ and Python with ROS or ROS2 development experience.
- Proficiency with AI coding agents.
- Experience with 6DoF object pose estimation, 3D Gaussian Splatting, NeRF, robot manipulation or teleoperation, automated annotation pipelines, or ground-truth generation is preferred.
- Published research in perception, pose estimation, or robotics is preferred.
Benefits
- Direct involvement in producing high-quality robot training data for embodied intelligence.
- Work alongside robotics engineers and machine-learning researchers.
- Access to proprietary hardware platforms including humanoid robots, camera arrays, and data gloves.
- Fast-paced, high-autonomy 0→1 work environment.
Categories
ML EngineeringRobotics
About XDOF
XDOF builds data, software, and operational infrastructure for robotics companies and research labs developing general‑purpose robots and physical AI. It provides scalable data collection, tooling, hardware integration, and services that help teams train, deploy, and manage autonomy systems. The company is privately held and positions itself as an infrastructure partner to frontier labs and robotics builders, with a team drawing experience from Tesla, Google, Applied Intuition, BAIR, Covariant, and Gatik.