Research Scientist, Fintech LLM - Rakuten Institute of Technology Worldwide (RIT) (Tokyo)

Job Description:

Business Overview

The Rakuten AI & Data Division (AIDD) creates powerful, customer-focused search, recommendation, data science, advertising, marketing, pricing, and inventory optimization solutions for various businesses in commerce, fintech, and mobile industries.   

Department Overview

Rakuten Institute of Technology Worldwide (RIT), the AI R&D engine of Rakuten Group, is a global network of research labs spanning Tokyo, Singapore, Boston, San Mateo, Bengaluru, and Paris. We are dedicated to pioneering advancements in core AI technologies, with a focus on machine learning, deep learning, and generative AI. Our researchers are actively exploring the use case of large language models, intelligent agent systems, and other cutting-edge applications, driving innovation across Rakuten's diverse ecosystem.

Fintech LLM is a new research initiative within RIT that explores the application of Large Language Models (LLMs) to Rakuten Group’s diverse fintech services, such as credit cards, banking, securities, and insurance. Our mission is to leverage recent advances in generative AI to unlock new possibilities in financial technology - from improving customer experiences to enhancing internal processes and decision-making.

As a member of the Fintech LLM team, you will be engaged in open-ended research and prototyping activities that investigate how LLMs can be effectively used in real-world fintech settings. This includes exploring techniques such as prompt engineering, fine-tuning, retrieval-augmented generation (RAG), and multi-step reasoning. You will collaborate closely with other researchers, engineers, and product teams to identify impactful use cases and validate innovative ideas.

This team offers a rare opportunity to work at the intersection of cutting-edge AI research and the complex, high-impact domain of financial services. Our environment encourages rapid experimentation, knowledge sharing, and scientific contribution, while maintaining a strong focus on real-world applicability within one of Rakuten’s most strategically important business areas.

Position:

Why We Hire

We are seeking research scientists who are passionate about LLMs and their practical applications in fintech. You will be expected to bring both technical depth and creativity to solve complex, language-centric problems in a high-stakes industry.

Position Details
- Define research goals and medium-term strategies aligned with business needs.
- Design, implement, and evaluate LLM-based algorithms to address key challenges in fintech services.
- Collaborate with product, engineering, and compliance teams to validate and refine solutions.
- Prototype novel approaches and support their integration into production systems.
- Mentor junior researchers and contribute to a collaborative research culture.
- Contribute to scientific communities via publications, patents, and conference participation.

Mandatory Qualifications:
- Ph.D. in Computer Science, NLP, Machine Learning, or a related field; or equivalent practical experience.
- Proficiency in Python and experience with ML/LLM frameworks (e.g., HuggingFace Transformers, OpenAI API, LangChain).
- Solid understanding of LLM architectures, prompt engineering, and fine-tuning techniques.
- Ability to work independently, communicate research clearly, and collaborate in cross-functional teams.
- Familiarity with Unix/Linux development environments and version control (e.g., Git).
- Strong interest in applying LLMs to structured/unstructured financial data.

Desired Qualifications:
- Track record of publications or open-source contributions in NLP or LLM research.
- Experience with instruction tuning, LoRA, or retrieval-based architectures.
- Experience with financial data (e.g., credit card transactions, financial documents, compliance workflows, financial markets).
- Ability to communicate in Japanese with product or business teams.
- Experience managing or mentoring junior researchers.
- Experience optimizing LLM applications for cost, latency, or accuracy in production settings.

Other Information:

Additional information on English Qualification

English: Fluent

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