Associate Robot Controls Engineer

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.

• 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

• 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

• 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)

• 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