Research Scientist: Multimodal Learning for Embodied Intelligence

Research Scientist:

Multimodal Learning for Embodied Intelligence Honda Research Institute USA San Jose, CA Job Details 2 hours ago Qualifications AI models Evaluation research Academic research projects Tooling Machine intelligence Technical research projects Research data analysis Model training Machine learning (ML) fundamentals Model evaluation

Full Job Description Job Number:

P25F11 Honda Research Institute USA (HRI-US) is seeking a Research Scientist to push the frontiers of machine learning for embodied, real-world intelligence. This is a research-focused role aimed at building systems that learn from diverse data at scale, generalize robustly to novel environments, and adapt continuously as conditions evolve. The successful candidate will develop multimodal and foundation-model-based approaches that unify perception, reasoning, and decision-making for embodied AI systems. San Jose, CA Key Responsibilities 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. Minimum Qualifications 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. Bonus Qualifications Expertise in one or more of the following areas: 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. Desired Start Date 7/6/2026 Position Keywords Machine Learning, Embodied Intelligence, multimodal systems, Memory and long-horizon reasoning

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