Robotics Simulation Engineer

About AIRoA

AI Robot Association (AIRoA) is an organization dedicated to collecting large-scale real-world robot data, including data from humanoid robots, and advancing the development of generative AI foundation models for robotics.

AIRoA has been selected as a project operator for the development of a data platform for generative AI foundation models in robotics under the “Post-5G Information and Communication Systems Infrastructure Enhancement R&D Project” by Japan’s Ministry of Economy, Trade and Industry (METI) and NEDO. The project budget is 20.5 billion yen. Based on this platform, AIRoA is carrying out a project to collect approximately one million hours of humanoid robot operation data using more than 100 robots, and to develop a world-class Vision-Language-Action (VLA) model using that data.

The significance of this initiative lies not only in the scale of its real-world data and humanoid platforms. AIRoA aims to make datasets, trained models, benchmarks, and evaluation environments as open as possible, building a “Robot Data Ecosystem” that can be commonly used by researchers, startups, and large enterprises. Achieving this requires not only AI models and cloud infrastructure, but also the capability to operate physical robots reliably and integrate sensors, actuators, control systems, and communication systems with a high degree of reliability through embedded software and firmware.

In this position, you will be responsible for firmware and embedded software development for humanoids, dual-arm robots, robotic hands, teleoperation and leader–follower systems, and robot systems for data collection used in AIRoA’s research projects.

Responsibilities

• Simulation infrastructure: Design and implement simulation environments, scenarios, evaluation tools, and log replay functions for humanoids, mobile robots, and service robots.
• Automated testing and CI: Establish simulation-based software testing, automated regression testing, and CI/CD integration to detect performance degradation and safety issues caused by software changes at an early stage.
• Evaluation scenarios: Build a scenario library and benchmark suite that reproduce real-world tasks, failure cases, sensor abnormalities, contact, crowded environments, robot state transitions, and related situations.
• Synthetic data and sensor simulation: Prepare synthetic data, sensor simulation, domain randomization, and log-derived scenarios for use in perception, navigation, and VLA evaluation.
• Sim-to-real analysis: Measure differences between physical robot logs and simulation results, and improve models, sensors, control systems, environment representations, and evaluation metrics.

Required Qualifications

• Experience developing or operating robotics, autonomous driving, factory automation, game engine, physics simulation, or high-fidelity simulation environments.
• Professional experience using simulators such as Isaac Sim, MuJoCo, Gazebo, PyBullet, Unity, or Unreal Engine.
• Experience using Python for evaluation pipelines, data processing, simulation tools, log analysis, visualization, and test automation.
• Experience designing mechanisms that development teams can continuously use, such as test cases, evaluation metrics, log replay, benchmarks, CI, and automated reports.
• Ability to understand one or more of the following areas and translate them into simulation requirements: sensors, control, state transitions, navigation, manipulation, or real-world robot constraints.

Preferred Qualifications

• Ability to organize requirements across multiple teams such as Autonomy, VLA, Navigation, Integration, and Hardware, and implement simulation environments as a shared platform.
• Experience with cloud-based simulation infrastructure, GPU-accelerated simulation, containerization, distributed execution, and job management.
• Experience with imitation learning, reinforcement learning, ML policy training, VLA evaluation, or synthetic data pipelines.
• Experience with ROS/ROS2, bag/log replay, Nav2, MoveIt, robot descriptions, sensor plugins, or simulation-to-real integration.
• Experience with Gaussian splatting, NeRF, world models, learned sensor models, diffusion-based methods, or neural rendering.

Benefits

There are currently no comparable projects in the world that collect data and develop foundation models on such a large scale. As mentioned above, this is one of Japan’s leading national projects, supported by a substantial investment of 20.5 billion yen from NEDO.

This position will play a crucial role in determining the success of the project. You will have broad discretion and responsibility, and we are confident that, if successful, you will gain both a great sense of achievement and the opportunity to make a meaningful contribution to society.

Furthermore, we strongly encourage engineers to actively build their careers through this project—for example, by publishing research papers and engaging in academic activities.