Job Description
At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $400M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics a reality.
We're looking for a Robot Control Engineer to develop and maintain the control systems that run on our humanoid robots. You'll own the software that connects hardware to behavior — from low-level control loops and actuator interfaces to state estimation and real-world deployment.
What You'll Do
Develop and maintain onboard control software including control loops, state estimation, and actuator interfaces
Implement and tune motion planning and control systems — including whole-body control, trajectory optimization, or model predictive control
Collaborate with the AI/ML team to integrate learned control policies onto real hardware reliably and efficiently
Contribute to system reliability, fault detection, and recovery logic for robust real-world operation
Work directly with prototype hardware, debugging discrepancies between simulated and real behavior and iterating on both
Support bring-up and field testing of new hardware revisions
What We're Looking For
4+ years of experience building control software for robotic systems or a closely related field
Strong software engineering fundamentals in C++, Python, or Rust
Hands-on experience with core robotics control: motion planning, control loops, state estimation, or actuator interfaces
Experience with ROS/ROS2 or similar robotics middleware in production or research contexts
Ability to debug across the full stack — from driver-level issues to control behavior on live hardware
Comfort working directly with physical hardware in fast-moving, ambiguous environments
Nice to Have (But Not Required)
Background in whole-body control, trajectory optimization, or model predictive control
Experience building sim-to-real pipelines for reinforcement learning or imitation learning policy training
Familiarity with humanoid or legged robot platforms and the unique modeling challenges they present
Exposure to learned perception or control models and their integration into real-time robot stacks
Prior work on early-stage hardware programs (prototype or pre-production robots)
Why This Role
Your code runs on the robot — the control software you write has a direct, physical impact on what our humanoids can do in the real world
Work at the boundary of hardware and AI, integrating state-of-the-art control and learned policies onto real robotic systems
High ownership in a small, fast-moving team where your contributions shape how the next generation of humanoid robots operate in production