Robotics & Control Engineer

Location Palo Alto Employment Type Full time Department Software At Rhoda AI, we build the full-stack foundation for humanoid robots — from high-performance, software‑defined hardware to foundational models and video world models that control it. Our robots are generalists capable of operating in complex, real‑world environments and handling scenarios unseen in training. We work at the intersection of large‑scale learning, robotics, and systems with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We are building a new computing platform for physical work, aggressively investing in R&D, hardware development, and manufacturing scale‑up. 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 #J-18808-Ljbffr Rhoda AI