What You'll Do
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
What You Bring
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
What Sets You Apart
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
Skills Required
- Hands-on experience with robotic manipulation on physical hardware (simulation-only backgrounds not accepted)
- 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
- Comfort debugging cross-domain issues spanning software, sensors, actuators, end-effectors, calibration, timing, and field conditions
- MS or PhD in Robotics, Mechanical Engineering, Computer Science, or related field (MS/PhD preferred; BS considered)
- Experience with humanoid platforms, dexterous hands, or contact-rich manipulation on real robots
- Background in robot learning applied to physical hardware (imitation learning, RL, task and motion planning) validated on real robots
- Experience taking a manipulation capability from prototype to reliable, repeatable field behavior
- Experience building evaluation frameworks, test suites, and metrics for manipulation success, repeatability, and failure recovery
What We Do
FieldAI is pioneering the development of a field-proven, hardware agnostic brain technology that enables many different types of robots to operate autonomously in hazardous, offroad, and potentially harsh industrial settings – all without GPS, maps, or any pre-programmed routes.
