Research Intern: Lifelong Spatial AI Explorer

Honda Ri; San Jose (CA); On-site; USD 34,000 - 55,000; Part time

Job summary

Honda Research Institute USA in San Jose, CA is offering a Research Intern position focused on Life-Long Embodied Spatial Reasoning. The internship targets building capable agents that continually build, update, and reason over spatial knowledge across episodes, integrating memory for planning and action in dynamic environments.

The role spans exploration of embodied navigation, 3D scene representation, continual learning, long-term memory systems, and multimodal foundation models, with hands-on

Qualifications

• PhD or near completion in CS/AI/Robotics.
• Extensive research background in CS, deep learning and 3D computer vision.
• Strong programming skills in Python with ML libraries (PyTorch, TensorFlow).

Responsibilities

• Conduct research on spatial reasoning and long-context video understanding.
• Review and reproduce relevant state-of-the-art research.
• Design and implement novel models, representations or memory architectures.
• Develop evaluation tasks and benchmarks for long-horizon spatial reasoning.
• Train and evaluate models using large-scale video and multimodal datasets.
• Analyze model behavior and identify limitations in spatial and temporal reasoning.
• Collaborate with researchers and engineers to prototype and validate research ideas.
• Communicate research findings through presentations, technical reports, and publications.

Skills

Python

Deep learning

3D computer vision

Education

Ph.D. in CS/AI/Robotics

Tools

PyTorch

TensorFlow

Job description

Honda Research Institute USA in San Jose, CA is offering a Research Intern position focused on Life-Long Embodied Spatial Reasoning. The internship targets building capable agents that continually build, update, and reason over spatial knowledge across episodes, integrating memory for planning and action in dynamic environments.

The role spans exploration of embodied navigation, 3D scene representation, continual learning, long-term memory systems, and multimodal foundation models, with hands-on