Senior Perception Engineer

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. Core Responsibilities Human Pose Estimation - 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 Robot Perception & Calibration - 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) Perception Model Training & Deployment - 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 End-to-End Loop from Data Collection to Model Delivery - 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 Requirements Must-Have - 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 Nice to Have - 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 What We Offer - 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