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Toyota Research Institute

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Staff Research Scientist, Culture & Systems Dynamics

Job Description

At Toyota Research Institute (TRI), we’re on a mission to improve the quality of human life. We’re developing new tools and capabilities to amplify the human experience. To lead this transformative shift in mobility, we’ve built a world-class team in Automated Driving, Energy & Materials, Human-Centered AI, Human Interactive Driving, Large Behavior Models, and Robotics.


The Team
The Harmonious Communities team at Toyota Research Institute (TRI) is building an AI-driven Toyota People System—a scientific and ethical foundation for understanding and improving the social dynamics that shape collective wellbeing and performance. We combine social and behavioral science, complex systems theory, AI / machine learning, and Human-Computer Interaction to uncover how culture, trust, and organizational structures influence human flourishing. Our goal is to design interventions and intelligent tools that help teams and societies thrive in harmony with technology.

The Opportunity
We are seeking a Staff Research Scientist to lead cross-disciplinary research on culture, systems dynamics, and intervention design in complex adaptive environments. You will develop and evaluate interventions that strengthen system-level health—such as trust, safety, inclusion, and resilience—particularly where success is defined by the absence of breakdowns or by subtle shifts in collective behavior. You’ll apply ethnography, causal reasoning, and computational modeling to identify leverage points for positive change and to quantify the cultural factors that sustain high-functioning systems over time. This is an ideal role for a researcher who thrives at the intersection of culture and computation, and who is eager to translate social-scientific insight into scalable AI systems that augment human potential.

Responsibilities

  • Design and evaluate real-world interventions (e.g., cluster or step-wedge trials, policy pilots, network interventions) to improve workforce wellbeing, safety culture, and trust across Toyota and partner organizations.
  • Integrate ethnographic and behavioral data into computational representations of social systems, using methods such as causal inference, social network analysis, or agent-based modeling.
  • Model diffusion and adoption dynamics—how culture, norms, and trust propagate through teams and organizations.
  • Collaborate with machine learning and AI researchers to embed cultural and organizational constructs in the Toyota People System’s architecture.
  • Develop ethical, evidence-based measurement frameworks for cultural health, burnout resilience, and collective intelligence.
  • Translate findings into action through technical reports, peer-reviewed publications, and design recommendations that inform human-centered AI products and organizational strategies.
  • Advance responsible research practices, including IRB and open-science principles, for human-subjects work in organizational settings.
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    Qualifications

  • Ph.D. or equivalent experience in Anthropology, Behavioral or Social Science, Implementation Science, or related fields.
  • Demonstrated expertise in applied systems research, including designing and evaluating interventions in complex, real-world environments.
  • Experience integrating qualitative and quantitative methods (ethnography, surveys, administrative data, causal modeling).
  • Proven ability to conceptualize and measure system-level outcomes (e.g., culture change, burnout reduction, safety climate, equitable access).
  • Strong analytical and computational skills (e.g., R, Python, simulation modeling, causal inference).
  • Experience leading multi-stakeholder, cross-functional research programs.

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    Bonus Qualifications

  • Experience with social-network and diffusion modeling, behavioral nudges, or implementation science in organizational or public systems.
  • Track record of translating research into practice—for instance, policy or organizational interventions that improved system performance or wellbeing.
  • Expertise in ethical and open-science practices for complex, human-centered research.
  • Publications or projects bridging culture and technology, health and systems, or anthropology and AI.
  • The pay range for this position at commencement of employment is expected to be between $208,000 and $312,000/year for California-based roles. Base pay offered will depend on multiple individualized factors, including, but not limited to, business or organizational needs, market location, job-related knowledge, skills, and experience. TRI offers a generous benefits package including medical, dental, and vision insurance, 401(k) eligibility, paid time off benefits (including vacation, sick time, and parental leave), and an annual cash bonus structure. Additional details regarding these benefit plans will be provided if an employee receives an offer of employment.

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    TRI is fueled by a diverse and inclusive community of people with unique backgrounds, education and life experiences. We are dedicated to fostering an innovative and collaborative environment by living the values that are an essential part of our culture. We believe diversity makes us stronger and are proud to provide Equal Employment Opportunity for all, without regard to an applicant’s race, color, creed, gender, gender identity or expression, sexual orientation, national origin, age, physical or mental disability, medical condition, religion, marital status, genetic information, veteran status, or any other status protected under federal, state or local laws.

    It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability. Pursuant to the San Francisco Fair Chance Ordinance, we will consider qualified applicants with arrest and conviction records for employment.

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    About the job

    Posted on

    Nov 6, 2025

    Apply before

    Dec 6, 2025

    Job typeFull-time

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