Research Scientist

Company Overview And Recruitment Background Equmenopolis Inc. is on a mission to revolutionize human-AI coevolution. We are pioneering an AI-driven diagnostic assessment and learning assistance platform, unlocking human potential and transforming education and business sectors. Our flagship product, LANGX, is a next-generation AI-powered language learning system that analyzes and enhances English proficiency through dynamic conversations with AI agents. Initially adopted as part of Waseda University’s official English curriculum, its impact is now rapidly expanding across educational institutions nationwide in Japan. Recognized for its groundbreaking innovation, LANGX has secured prestigious global accolades, including the Best Paper Award at SIGDIAL 2024, the SXSW EDU Launch 2023 Finalist Selection, the University Startup Award 2024, and the JEITA Startup Award establishing Equmenopolis as a global leader in AI-powered assessment and learning technologies. As we scale, we are seeking exceptional talent to drive the evolution of our platform. Our R&D team is an elite force of top-tier specialists in multimodal conversational AI, natural language processing, linguistics, learning science, digital humans, data science, software engineering, UI/UX and project management. With experts from over 10 countries, we are an international powerhouse, harnessing diverse expertise to develop our innovative products. As AI evolution accelerates at an unprecedented pace, Equmenopolis is redefining the relationship between humans and AI, taking on the challenge of transforming society itself. For engineers, researchers, and designers,

we offer
an exhilarating environment that goes beyond mere technological development — an opportunity to shape the next society. Main Responsibilities This position is responsible for analyzing and describing the very nature of human-AI interaction, and building an evaluation infrastructure to measure and improve its "quality." Rather than treating conversation as mere text data, this role approaches it as a process of social interaction—encompassing turn structure, timing, responsiveness, and coordination—and systematizes how to design and evaluate dialogue between AI agents and humans by leveraging insights from Computational Linguistics and Conversation Analysis. Dialogue quality is fundamental to the learning support and diagnostics of the EQU AI platform, and this position will lead the development of the core theoretical and analytical framework that underpins it.
• Collaborate with a team of applied linguists, dialog system, and machine learning researchers, data engineers, and content writers to design and establish our platform for dialog quality assurance.
• Conceptualize an evaluation framework for multimodal dialog quality assurance from (applied) linguistics perspectives.
• Operationalize a series of quantitative metrics to gauge our conversation quality for assessment and learning purposes.
• Design and implement a workflow to improve user experiences in dialogue by extracting features, constructing feedback report, and coordinating with conversational AI researchers to improve our multimodal AI agent.
• Communicate findings both internally and externally through internal team meetings, conference presentations, and publications. Required Qualifications And Skills
• Ph.
D. in linguistics, applied linguistics, computational linguistics, natural language processing, or equivalent.
• Publication record on computational analysis of interaction or communicative success.
• Demonstrated excellence in analyzing interactional data as co-constructed activity between participants through computational approaches (e.g., natural language processing).
• Demonstrated ability to translate qualitative findings (e.g., repair sequences, timing, multimodal coordination) into measurable features or coding schemes for computational modeling.
• Demonstrated ability in conceptualizing and operationalizing dialog quality metrics based on the overarching goals of our product, goals of specific interactional tasks, extant literature, and technical constraints.
• Excellent skills in data science tools, particularly Python and R.
• Demonstrated skills in analyzing structured quantitative data of conversation using descriptive and inferential statistics—in Python (pandas or equivalent), R (tidyverse or equivalent), and SQL.
• Excellent interpersonal, communication, and organizational skills for effect Desirable Qualifications
• Experience conducting qualitative analyses of spoken or multimodal interaction using Conversation Analysis, Interactional Linguistics, or related frameworks.
• Demonstrated ability to design and validate computational pipelines for detecting or predicting dialog quality metrics (e.g., turn-taking smoothness, responsiveness, alignment).
• Experience conceptualizing and administering a project to build multimodal corpora with annotations for machine learning (e.g., transcription, annotation, synchronization of audio–video–text data).
• Experience publishing or presenting research on dialog quality, interactional competence, or human–AI communication.
• Familiarity with deep learning or speech technologies (e.g., PyTorch, Hugging Face) is a plus.
• Familiarity with the development and validation of rating scales for rater-mediate assessment is a plus. Ideal Candidate Profile
• A researcher who seeks to understand the nature of human conversation and apply such insights to improving AI dialog systems.
• Someone who views conversation not merely as text but as a form of social interaction, and who is motivated to analyze its linguistic, temporal, and multimodal structure.
• A scholar–practitioner who is equally interested in theoretical inquiry and practical implementation, and who can bridge linguistic theory, computational modeling, and system design.
• A collaborator who values interdisciplinary teamwork and contributes to the creation of new evaluation frameworks and analytic infrastructures.
• A creative thinker who aims to integrate knowledge from conversation analysis, computational linguistics, and machine learning to advance the science of human–AI interaction. Employment Type Full-time employee / Contract employee / Freelance Work Location 3F, Green Computing Systems Research Center, 27 Waseda, Shinjuku-ku, Tokyo Nearest station: 1-minute walk from "Waseda Station" on Tokyo Metro Tozai Line Work Style Hybrid Desired Start Date Anytime Visa Sponsorship Visa sponsorship available Company Details Headquarters Entrepreneurship Center, 1-22-3 Nishi-Waseda, Shinjuku-ku, Tokyo Research Lab 3F, Green Computing Systems Research Center, 27 Waseda, Shinjuku-ku, Tokyo Established May 2, 2022 CEO Yoichi Matsuyama Main Business Development of conversational AI agent platforms and their applications URL https://www.equ.ai/en/

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