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 Software Engineer to build and validate the simulation environments that underpin our humanoid robotics platform. You'll develop physics-based models that closely mirror real hardware, and own the software pipelines that bridge simulation and the physical world — from motion planning and control to sim-to-real transfer for AI policy training. This is a high-impact role on a small team building foundational technology for Gen 0 and Gen 1 robot programs.

What You'll Do

• Build and maintain simulation environments for our humanoid robot platforms, including physics-based models (e.g., MuJoCo, IsaacSim, PyBullet, or similar) that closely match real hardware behavior

• Develop and validate robot software — including motion planning, control loops, state estimation, and actuator interfaces — in simulation before deployment to physical systems

• Integrate simulation pipelines with the broader software stack: perception, teleoperation, logging, and data collection infrastructure

• Collaborate with the AI/ML team to build sim-to-real pipelines that accelerate policy training and evaluation

• Work directly with prototype hardware, debugging discrepancies between simulated and real behavior and iterating on both

• Contribute to software architecture decisions for our growing robot software platform across multiple robot programs

• Write production-quality code that other engineers can build on: clean interfaces, good documentation, and testable components

What We're Looking For

• 4+ years of experience in robotics software engineering or a closely related field

• Proficiency with at least one major robotics simulation platform (MuJoCo, IsaacSim, PyBullet, Gazebo, or similar)

• Strong software engineering fundamentals — production-quality Python and/or C++, clean interfaces, and a commitment to testable, well-documented code

• Hands-on experience with core robotics software: motion planning, control loops, state estimation, or actuator interfaces

• Experience integrating software components across a complex stack — connecting simulation to perception, logging, or data collection systems

• Comfort working directly with physical hardware and debugging sim-to-real discrepancies

• Strong communication and collaboration skills — able to work closely with both hardware and AI/ML teammates

Nice to Have (But Not Required)

• 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

• Background in whole-body control, trajectory optimization, or model predictive control

• Experience with ROS/ROS2 or similar robotics middleware in production or research contexts

• Prior work on early-stage hardware programs (prototype or pre-production robots)

• Contributions to open-source robotics simulation tooling or research publications in robotics or robot learning

Why This Role

• Own the simulation layer that bridges AI research and physical hardware — your work directly determines how fast the team can iterate on robot behavior before touching real hardware

• Work across the full stack alongside AI/ML researchers, perception engineers, and hardware teams on Gen 0 and Gen 1 programs that define the foundation of the platform

• High ownership on a small team building genuinely novel technology, with direct access to prototype hardware and a tight feedback loop between simulation and reality