Overview
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build ris
Full job description
FieldAI’s Irvine team is where embodied AI meets real robots, real sensors, and real field deployments. Based in the heart of Southern California’s robotics ecosystem, we build risk-aware, reliable, field-ready AI systems that solve the hardest problems in robotics and unlock the full potential of embodied intelligence. If you want your work to ship, get tested on hardware, and improve through real deployments, Irvine is the place. We go beyond typical data-driven approaches or pure transformer-only architectures, combining rigorous engineering with learning systems proven in globally deployed solutions that deliver results today and get better every time our robots run in the field.
The Humanoid Engineer, Manipulation will develop, integrate, test, and improve manipulation capabilities on humanoid robots. This role sits at the intersection of robot learning, controls, perception, teleoperation, and field deployment. The engineer will translate manipulation research into robust capabilities that work on physical humanoid platforms and improve through real-world testing.
Design, implement, and evaluate humanoid manipulation and loco-manipulation behaviors on real hardware
Integrate perception, planning, control, grasping, whole-body coordination, and task execution into deployable robot workflows
Run hardware experiments, analyze failures, and improve manipulation reliability across diverse objects, environments, and tasks
Partner with system integration, hardware, field application, and testing teams to move capabilities from prototype to deployment
Support teleoperation, data collection, and human-in-the-loop workflows for improving manipulation performance
Build tools, metrics, and evaluation protocols for manipulation success, repeatability, failure recovery, and operator usability
Debug cross-domain issues spanning software, sensors, actuators, end-effectors, calibration, timing, and field conditions
MS or PhD in Robotics, Mechanical Engineering, Computer Science, or a related field preferred; BS considered with a demonstrated track record of hands-on robotics work across multiple physical systems — research projects, competition robotics, or internships with daily hardware exposure
Hands-on experience with robotic manipulation, humanoids, mobile manipulation, dexterous hands, or contact-rich robotics — must include physical hardware; simulation-only backgrounds will not be considered
Strong foundation in kinematics, dynamics, motion planning, control, and real robot experimentation
Experience with C++, Python, ROS/ROS2, and Linux in a real robotics codebase
Demonstrated ability to iterate quickly from experiment to working behavior on physical hardware; comfortable running daily hardware experiments, analyzing failures, and adapting approach in real-time
Background appropriate for a junior-to-mid engineer; fresh MS and PhD graduates welcome
Experience with humanoid platforms or contact-rich, dexterous manipulation systems — you've worked with robots that have hands, not just grippers
Background in robot learning applied to physical hardware: imitation learning, reinforcement learning, or task and motion planning that you've validated on a real robot, not just in simulation
You've taken a manipulation capability from prototype to reliable, repeatable field behavior — you know what it takes to close that gap and you've done it
Track record of building evaluation frameworks for manipulation: test suites, metrics for success and failure, and the discipline to document and learn from what breaks
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