Research Engineer:

Robotics & 3D Simulation Honda Research Institute USA San Jose, CA Job Details Contract 6 hours ago Qualifications AI models Machine intelligence Machine learning (ML) fundamentals 3D modeling Robot simulation

Full Job Description Job Number:

P25T09 Honda Research Institute USA (HRI-US) is seeking a self-motivated research engineer to join our Intelligent Robotics Research division. This individual will support research workflows by preparing and running high-fidelity 3D simulation experiments in robotics, including but not limited to: reconstructing real-world objects and contact-rich scenes, aligning/evaluating simulated behavior with real-world measurements, adapting/evaluating existing machine learning and control methods using simulation. The role combines hands-on scene creation, robotics simulation, simulation-based methods, experimental validation, and clear communication of technical and implementation outcomes. San Jose, CA Key Responsibilities Create diverse contact-rich scenes in simulation using 3D scanners and/or 3D modelling of real-world objects. Perform system identification to reduce/evaluate sim-to-real gaps. Create and run scenes across different simulators to compare and evaluate simulation fidelity. Implement, adapt, and evaluate existing methods that are relevant to and leverage simulation (e.g., control optimization, 3D representation learning). Deliver results in accordance with project timelines. Prepare written and oral technical reports and demonstrations. Collaborate with our teams of scientists and engineers in Honda's regional and global R D offices.. Communicate results, tradeoffs, and implementation outcomes. Minimum Qualifications MS or PhD in Robotics, Computer Science, or related field. Experience with 3D asset creation or processing workflows, such as 3D scanning, mesh cleanup, CAD/Blender-style modeling, material definition, etc. Experience with creating high-fidelity 3D scenes and 3D assets of real objects. Experience with running robotics simulation (experience with multiple platforms preferred). Comfortable reading, understanding, and implementing algorithms from research papers. Has general knowledge/experience with machine learning (e.g., collecting data, training and evaluating models, defining loss, understanding of different architectures). Bonus Qualifications Experience with contact-rich physics simulation. Experience with 3D representation learning. Has knowledge and experience on system identification. Has general knowledge/experience with control optimization (e.g., multi-chain gradients and Jacobians, optimization steps and trust regions). Familiarity with numerical simulation, optimization, or differentiable physics workflows. Demonstrated expertise through research, projects, or open-source contributions. Desired Start Date 10/26/2026 Contract Duration 1 year Position Keywords robotics, 3D simulation, high-fidelity 3D modelling, sim-to-real, real-to-sim

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