Member of Technical Staff, System Integration

Job Description

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.

We're looking for a full-stack engineer who can work across the entire stack, from hardware, sensors, and data pipelines through to algorithm deployment, and who will independently own the end-to-end implementation of embodied AI capabilities on robots.

Responsibilities

• Maintain and iterate on the embodied AI data collection system, covering multimodal sensor synchronization, data pipelines, storage, and downstream annotation toolchains.
• Own the integration, calibration, and routine maintenance of camera systems (ZED / RealSense / industrial cameras, etc.) and robotic arms, keeping equipment stable and operational.
• Independently own end-to-end deployment of imitation learning / DAgger / teleoperation algorithms on robots, including data collection, model training, inference deployment, and on-site tuning.
• Drive data quality assurance mechanisms: timestamp synchronization, coordinate frame alignment, anomalous data detection, and rapid diagnosis and repair of collection failures.
• Define and maintain robot-side software interfaces and communication protocols (ROS2 / DDS, etc.) to enable fast replication of collection stations and deployment stations across platforms and sites.
• Support on-site data collection and algorithm deployment, produce technical documentation, and continuously improve collection efficiency and model performance.

Requirements

• Bachelor's degree or above in Robotics, Automation, Computer Science, or a related field, with 3+ years of relevant experience.
• Strong programming skills in Python / C++; familiar with system-level development and debugging in Linux environments.
• Expert in ROS / ROS2, including node communication mechanisms (Topics, Services, Actions), parameter management, and the Launch system.
• Familiar with camera system integration and calibration (intrinsics, extrinsics, multi-camera synchronization); hands-on experience with ZED / RealSense or similar RGB-D / stereo cameras.
• Familiar with robotic arm control interfaces (e.g., UR / Franka / xArm or in-house arms), with an understanding of the driver layer, kinematics, and high-level API calls.
• Familiar with common robot hardware interfaces (CAN, EtherCAT, RS485, USB) and sensor integration (IMU, depth cameras, force/torque sensors, etc.).
• Experience training and deploying deep learning models; proficient in PyTorch and able to independently handle the full workflow from data preparation and training to inference and deployment.
• Proficient in using AI coding tools (e.g., Claude Code, Cursor) in daily development, with a demonstrated ability to significantly boost engineering productivity through them.

Nice to Have

• Complete project experience in embodied AI / imitation learning data collection and algorithm deployment.
• Familiarity with training and deployment details of mainstream imitation learning algorithms such as Diffusion Policy, ACT, and VLA.
• Familiarity with robot data formats such as SVO2, ROS bag, HDF5, and LeRobot datasets.
• Familiarity with multi-device synchronization solutions such as camera hardware triggering and PTP time synchronization.
• Experience integrating devices such as VIVE Trackers, OptiTrack, and motion capture gloves.
• Experience with video encoding/decoding (H.264 / H.265 / Jetson hardware codecs).
• Fluent in written English, able to read research papers and open-source project documentation directly.

What We Offer

• End-to-end ownership of engineering deployment for embodied AI capabilities, with full autonomy from data to algorithms to deployment.
• A competitive compensation package.
• A flat, open engineering culture with genuine involvement in technical decisions.
• Well-equipped hardware lab resources supporting rapid prototyping and iteration.
• Flexible work arrangements, plus ongoing learning and internal tech-sharing programs.