At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be 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're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that 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
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