Research Scientist: Human-AI Co-Adaptation and Continual ...

Honda Research Institute USA, Inc.; San Jose (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Honda Research Institute USA, Inc. is seeking a researcher in San Jose, CA, to lead projects on continual, interactive learning for multimodal AI systems. The candidate should have a Ph.D. and expertise in areas like human-AI interaction or decision-making systems, plus a strong research record.

This role involves original research, collaboration with interdisciplinary teams, and contributions to publications and patents. Bonus qualifications include experience in uncertainty estimation and simulation platforms.

Qualifications

• 1 - 3 years of relevant work experience.
• Demonstrated ability to formulate and lead independent research programs.
• Deep expertise in continual learning, AI systems, or human-AI interaction.

Responsibilities

• Conduct original research on lifelong learning for multimodal AI systems.
• Develop adaptive methods for AI systems through human interaction.
• Collaborate with interdisciplinary teams and contribute to innovative research.

Skills

Research in machine learning

Strong communication skills

Collaborative teamwork

Education

Ph.D. in Computer Science or related field

Job description

Job Number: P25F10

San Jose, CA

Key Responsibilities

• Conduct original research on continual, interactive, and lifelong learning for multimodal AI systems.


• Develop methods that enable AI systems to adapt, personalize, and improve through interaction with human users.


• Investigate mechanisms for human-AI interactive learning, including lightweight adaptation, memory and retrieval, uncertainty-aware learning, and learning from human feedback.


• Develop algorithms that remain robust under distribution shifts, changing user populations, evolving tasks, and changing multimodal inputs.


• Advance methods for user modeling, personalization, and human-AI co-adaptation through repeated interaction.


• Design and utilize simulated and real-world environments to study adaptation, personalization, and human-AI collaboration.


• Design rigorous evaluation methodologies and benchmarks to measure adaptation, robustness, and long-term human-AI interaction.


• Conduct empirical analyses of adaptation dynamics, including sample efficiency, calibration, stability, and catastrophic forgetting.


• Collaborate with interdisciplinary teams and contribute to publications, patents, prototypes, and research innovations.

Minimum Qualifications

• Ph.D. in Computer Science, Robotics, Electrical Engineering, Machine Learning, Artificial Intelligence, or a related field.


• Strong research record in machine learning, artificial intelligence, robotics, or related disciplines, demonstrated through publications, impactful projects, or equivalent research contributions.


• Demonstrated ability to formulate, lead, and execute independent research programs.


• Deep expertise in one or more of the following areas:
  
  
  
  • Continual learning, online learning, test-time adaptation, personalization, or learning from human feedback.
  
  
  • Multimodal AI systems, including representation learning, alignment, grounding, or reasoning across modalities.
  
  
  • Human-AI interaction, human-aware AI, assistive AI, or interactive learning systems.
  
  
  • Embodied AI, decision-making systems, or adaptive AI systems operating in real time.
  
  


• Strong communication, presentation, and collaboration skills.


• 1 - 3 years of relevant work experience.

Bonus Qualifications

• Experience with continual learning, online learning, test-time adaptation, memory-augmented systems, retrieval-augmented systems, or learning from human feedback.


• Experience with personalization, user modeling, adaptive assistants, or long-term human-AI interaction.


• Experience with uncertainty estimation, calibration, and robust adaptation in multimodal systems.


• Experience with multimodal reasoning, agentic systems, planning, or decision-making in collaborative human-AI environments.


• Experience with simulation and benchmarking platforms for embodied AI, human-AI interaction, or multi-agent systems.


• Publication record at leading venues such as NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ACL, EMNLP, AAAI, RSS, or CoRL.

Desired Start Date

7/6/2026

Position Keywords: Machine Learning, Continual Learning, multimodal AI, human-AI co-adaptation