AI Research Scientist: Computational Social Systems ...

Honda Research Institute USA, Inc.; San Jose (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

Honda Research Institute USA, Inc. is looking for an AI Research Scientist in San Jose, CA, to develop machine learning and simulation methods for forecasting complex social systems. The ideal candidate will build models that help assess impacts before deploying AI technologies at scale.

This role involves collaboration across various fields, requiring strong expertise in AI, computational modeling, and the ability to communicate complex insights effectively. Qualifications include a Ph.D. in a related field and deep knowledge in AI methods.

Qualifications

• Ph.D. in relevant quantitative field with strong computational training.
• Research background in AI/ML and computational modeling.
• Ability to evaluate models using empirical data.

Responsibilities

• Develop AI and computational modeling methods for social systems.
• Build complex simulation models such as multi-agent and network-based.
• Collaborate with interdisciplinary researchers.

Skills

AI/ML Background

Computational Modeling

Simulation Techniques

Programming in Python

Education

Ph.D. in Computer Science or related field

Tools

AI/ML Frameworks

Job description

Overview

Honda Research Institute USA (HRI-US) is seeking an AI Research Scientist to develop machine learning, simulation, and computational modeling methods for understanding and forecasting complex social systems shaped by AI-enabled technologies. This role is intended for an AI scientist who applies advanced AI methods to computational social system modeling. The scientist will build models that help answer forward-looking “what-if” questions before AI-enabled technologies are deployed at scale. Example questions include: How might AI agents change collaboration, coordination, and decision-making in teams? How could AI-enabled systems influence mobility, emergency response, education, community planning, or public services? How do trust, adoption, reliance, information diffusion, equity, resilience, and unintended consequences evolve when AI becomes embedded in everyday social systems? The ideal candidate can translate complex real-world social systems into formal computational models, calibrate and validate those models using empirical data, and communicate insights clearly to technical and interdisciplinary audiences. The role requires both fundamental AI research capability and functional impact: the scientist should advance AI-based modeling and simulation methods while creating prototypes, scenario-analysis tools, and decision-support frameworks that inform future technology development. The scientist will collaborate with researchers across AI, robotics, human-AI interaction, cognitive modeling, behavioral science, and systems science to create methods for modeling the long-term impact of AI-enabled technologies across scales, from small-group interaction to community-level and society-level outcomes.

San Jose, CA

Key Responsibilities

• Develop AI and computational modeling methods for simulating social, organizational, community, and socio-technical systems.
• Build multi-agent, network-based, causal, probabilistic, system-dynamics, complex-systems, generative agent, or hybrid simulation models.
• Design counterfactual and scenario-based simulations to compare alternative AI deployment strategies, policies, interventions, and system designs.
• Model social adaptation mechanisms such as trust, adoption, reliance, coordination, norms, learning, information diffusion, equity, resilience, and emergent collective outcomes.
• Apply and advance AI methods, including foundation models, LLM-based agents, multi-agent AI systems, generative simulation, graph learning, causal modeling, and uncertainty-aware inference.
• Integrate empirical datasets into modeling pipelines, including behavioral logs, mobility data, survey data, experimental data, public administrative data, demographic data, and human-AI interaction data.
• Evaluate model performance, uncertainty, sensitivity, assumptions, limitations, and implications using appropriate validation method.
• Collaborate with experimental and inter-disciplinary researchers to connect model predictions with human-subject studies, field data, and system-level evidence.
• Translate research results into publications, prototypes, technical reports, scenario-analysis tools, and internal decision-support systems.
• Contribute to HRI’s long-term research strategy on responsible, human-centered, and socially aware AI systems.

Minimum Qualifications

• Ph.D. in computer science, artificial intelligence, machine learning, computational social science, complex systems, systems engineering, statistics, economics, cognitive science, public policy with strong computational training, or a related quantitative field.
• Strong research background in AI/ML, computational modeling, simulation, or data-driven modeling of social, behavioral, organizational, or socio-technical systems.
• Demonstrated ability to formulate complex real-world systems as computational models and evaluate those models using empirical data.
• Expertise in at least one of the following areas: multi-agent systems, agent-based modeling, generative-agent simulation, causal inference, counterfactual modeling, graph or network modeling, probabilistic or Bayesian modeling, or simulation-based forecasting.
• Experience applying AI/ML methods to modeling, prediction, simulation, decision support, or human/social behavior analysis.
• Strong programming skills in Python or a similar scientific computing language, with experience using AI/ML, simulation, statistical modeling, or data-science frameworks.
• Ability to communicate model assumptions, uncertainty, limitations, and implications to technical and interdisciplinary audiences.
• Demonstrated research output through peer-reviewed publications, prototypes, open-source tools, technical reports, patents, or applied research projects.

Bonus Qualifications

• Publication record in venues such as NeurIPS, ICML, ICLR, AAAI, AAMAS, CHI, CSCW, KDD, ICWSM, ACL, EMNLP, or comparable conferences and journals.
• Experience with LLM-based agents, generative agents, multi-agent AI systems, AI simulations of human behavior, or foundation models for social system modeling.
• Experience with large-scale computational modeling, counterfactual simulation, policy simulation, scenario-analysis tools, digital twins, or decision-support dashboards.
• Experience applying computational models to domains such as mobility, emergency response, education, public health, urban systems, workplace collaboration, community planning, or responsible AI deployment.
• Familiarity with behavioral science, decision science, social science theory, human factors, human-AI interaction, organizational modeling, or policy modeling.
• Experience working with real-world datasets, field data, experimental data, or human-subject research data.
• Ability to connect fundamental AI research to practical tools that support long-term technology strategy, responsible deployment, and measurable societal impact.

Desired Start Date 8/31/2026

Position Keywords

• AI for Computational Social Systems; Multi-Agent Simulation; Generative Agents; Large-scale Social System Modeling;
• Human-AI Dynamics; Counterfactual AI Impact Modeling; Causal Inference; Network Science; Complex Systems;
• Socio-Technical Systems; Responsible AI