Humanoid Engineer
XDOF
Humanoid Engineer
XDOF
Data pipelines and annotation for robotics
We are seeking a talented Humanoid Engineer to join our team to design, develop, and enhance next-generation humanoid robots. You will work cross-functionally to push the boundaries of robotics technology and create intelligent machines that interact seamlessly with humans in real-world environments.
Design, prototype, and test humanoid robotic systems, including mechanical, electrical, and software components.
Collaborate with interdisciplinary teams to define and refine system architecture, functions, and features for humanoid robots.
Develop and implement control algorithms for dexterous manipulation, locomotion, and human-robot interaction.
Integrate sensors, perception systems, and AI modules to enable robust operation in dynamic environments.
Perform simulation, validation, and iterative optimization to improve performance, safety, and reliability.
Document designs and results, working closely with manufacturing and field support teams to ensure successful deployment.
Bachelor’s degree or above in Mechanical Engineering, Robotics, Electrical Engineering, Computer Science, or a related field.
3+ years of experience developing robotics systems, ideally focusing on humanoid or anthropomorphic designs.
Strong knowledge of kinematics, dynamics, and robot control (movement, balance, and manipulation).
Hands-on experience with mechatronics, embedded systems, and sensor integration.
Proficiency in programming languages commonly used in robotics (e.g., C++, Python, ROS).
Familiarity with AI, machine learning, and perception for robotics applications is a plus.
Creative problem-solver with attention to detail and a passion for building impactful technologies.
Competitive salary and stock options
Health, dental, and vision insurance
Flexible work environment
Opportunities for advancement and professional growth
Work on groundbreaking technologies with a world-class team
XDOF builds data pipelines, data-collection tools, and annotation systems that frontier AI labs and robotics companies use to train robots to operate in the physical world. It tackles the data bottleneck in physical AI by combining teleoperation, simulation, and egocentric data collection, then layering in data cleaning, tooling, and annotation to prepare usable datasets. The company differentiates itself through its end-to-end data infrastructure for physical AI, its mix of teleoperation, simulation, and first-person data methods, and its Berkeley-founded background from 2024. Its goal is to enable robots to learn to operate in the real world by providing the pipelines and labels needed for training.
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