Member of Technical Staff, System Integration

XDOF; San Francisco (CA); On-site; USD 130,000 - 190,000; Full time

Benefits offered by this job

Ownership of deployment

Competitive compensation

Flat engineering culture

Hardware lab resources

Flexible work arrangements

Job summary

XDOF is seeking a full-time engineer to own end-to-end embodied AI capabilities on robots, spanning hardware, sensors, pipelines, and deployment. You will configure camera systems, calibrate multi-camera setups, and implement imitation learning and teleoperation workflows on-site, driving data quality and system stability.

The role requires strong Python/C++ skills, ROS2 expertise, and hands-on experience with RGB-D cameras and robotic arms.

Qualifications

• Bachelor’s degree or above in Robotics, Automation, CS, or related field.
• 3+ years of relevant experience.
• Proficient Python/C++ programming; Linux system-level development.
• Expertise in ROS/ROS2 with node communication (Topics, Services, Actions).
• Hands-on with camera systems (ZED/RealSense) and robotics arms.
• Experience training/deploying deep learning models (PyTorch).
• Familiarity with CAN, EtherCAT, RS485, USB sensor interfaces.

Responsibilities

• Maintain and iterate embodied AI data collection system with multimodal sensors and data pipelines.
• Integrate, calibrate, and maintain camera systems and robotic arms to stay stable and operational.
• Own end-to-end deployment of imitation learning and teleoperation algorithms on robots, including data collection and model training.
• Develop data quality checks: time sync, frame alignment, anomaly detection, rapid repair of collection failures.
• Define robot-side interfaces and ROS2/DDS communication to enable replication across platforms.
• Support on-site data collection and deployment, write technical docs, improve collection efficiency.

Skills

Python

C++

Linux development

ROS / ROS2

PyTorch

Camera systems integration

Robotic arms control

Data pipelines

Education

Bachelor’s degree in Robotics / Automation / CS

Tools

ZED

RealSense

UR

Franka

xArm

Job description

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.