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# Research Scientist: Multimodal Learning for Embodied Intelligence

- **Company:** HONDA RESEARCH INSTITUTE USA
- **Location:** San Jose, CA, United States
- **Experience:** Starter
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Computer Vision, Machine Learning, Large Language Models, Information Technology, Data Selection
- **Published:** June 21, 2026
- **Apply:** https://usa.honda-ri.com/-/research-scientist-multimodal-learning-for-embodied-intelligence?redirect=%2Fassociate-positions

## About the Role

* Ph.D. or M.S. with equivalent experience in Computer Science, Machine Learning, Artificial Intelligence, Robotics, or a related field.
 * Deep understanding of modern machine learning and deep learning techniques.
 * Experience training and evaluating models at scale.
 * Experience with foundation models, multimodal learning, or embodied AI systems.
 * Proficiency with modern research tooling, including AI-assisted development workflows and agent-based experimentation frameworks.
 * Strong ability to formulate research problems, execute experiments, analyze results, and communicate findings clearly.
 * 1 - 3 years of relevant work experience.

## Description

* Develop and advance machine learning models, including foundation models and multimodal systems that reason over vision, language, action, and other embodied signals.
 * Design model architectures that unify perception, reasoning, and decision-making for embodied intelligence.
 * Train, fine-tune, and evaluate models on large-scale, diverse datasets to improve robustness, generalization, and real-world performance.
 * Develop rigorous evaluation methodologies, benchmarks, and metrics for complex and potentially safety-critical AI systems.
 * Build reusable research infrastructure, including evaluation suites, analysis pipelines, and experimental frameworks.
 * Conduct empirical analysis of model behavior, generalization, failure modes, and adaptation under changing environments or distribution shifts.
 * Contribute to publications, patents, and research prototypes that demonstrate technical impact., + Continual and adaptive learning: lifelong learning, online learning, and adaptation under distribution shift.
 + Data-centric AI: curriculum learning, data selection, active learning, and methods for measuring and improving data quality.
 + Memory and long-horizon reasoning: persistent memory architectures, external memory systems, long-context modeling, and temporal abstraction.
 + Mechanistic interpretability: understanding and analyzing model internals, behaviors, and failure modes.
 * Strong publication record in leading machine learning, AI, computer vision, robotics, or NLP venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ACL, CoRL, RSS, or ICRA.
 * Interest in building robust embodied intelligence systems that can operate reliably in real-world environments.


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