CAREERS

The Senior Research Scientist for Theoretical Physics is responsible for the building of new research prototypes from scratch for their eventual productionization and for publishing new ideas in top research conferences. This role is part of the global Rakuten Institute of Technology (RIT), the corporate R&D department responsible for leading strategic technological progress. RIT collaborates with the Harvard Quantum Initiative to accelerate the application of quantum technology to industry, and as the part of this collaboration, the researchers will have the access to Harvard Department of Physics facilities as visiting scholars.

The Project & Product Management Group proactively communicates with researchers and business to deliver a strategic research roadmap across multiple business areas. The person in this role will handle a variety of tasks, including business model review, project management (budgeting, program roadmaps, lifecycle management), and support effective operations for research projects.

RIT Engineering identifies and provides the powerful ML infrastructure, agile ML tools and ML stores to perform faster iterations, to scale towards large models, and reproduce and reuse platform agnostic research.

Engineering Group works closely with RIT researchers to build production-adoptable AI prototypes and accelerate research in production.

The Language program, which is one of our core science programs, aims to automatically create a well-organized product catalog using advanced NLP and ML technologies to generate structured information from unstructured product titles, descriptions, and images. We are also interested in leveraging weakly/semi-supervised and unsupervised techniques to incorporate signals beyond traditional sources, such as customer behavior data, to augment our language understanding. We are looking for research scientists with a strong research background in the NLP field and strong motivation to join the Language program.

Rakuten Institute of Technology (RIT) is the dedicated Research and Innovation department of Rakuten, with teams in Tokyo, Paris, Boston, San Mateo, Singapore, and Bengaluru.

The Speech Processing Team of the Rakuten Institute of Technology is part of the Global Data Science Department within Rakuten Group, Inc. The team carries out applied research in the speech processing areas primarily in speech recognition and synthesis, keyword spotting, speaker verification, speech dialization, source-channel separation, and other potential areas involving speech and/or audio signals that would support a wide range of applications within the Rakuten ecosystem and its businesses.  Some examples of business use cases include Interactive Voice Response (IVR) systems and automatic transcription of customer engagements for quality enhancement purposes, to name a few.

Rakuten Institute of Technology (RIT) is the dedicated Research and Innovation department of Rakuten, with teams in Tokyo, Paris, Boston, San Mateo, Singapore, and Bengaluru.

The Speech Processing Team of the Rakuten Institute of Technology is part of the Global Data Science Department within Rakuten Group, Inc. The team carries out applied research in the speech processing areas primarily in speech recognition and synthesis, keyword spotting, speaker verification, speech diarization, source-channel separation, and other potential areas involving speech and/or audio signals that would support a wide range of applications within the Rakuten ecosystem and its businesses.  Some examples of business use cases include Interactive Voice Response (IVR) systems and automatic transcription of customer engagements for quality enhancement purposes, to name a few.

We have a huge amount of real business data, powerful and reliable computer environments, and brilliant scientists and engineers that empower people and society through AI technologies. We are hiring more research scientists to scale up our ability of empowerment. You will be excited that the cutting-edge technologies you develop have an enormous impact on real businesses. 

The Vision program, which is one of our core science programs, aims to understand and process images and videos using advanced computer vision and machine learning. We are working areas such as estimating product attributes for large-scale image datasets for creating catalogs, robust document image understanding for smooth service onboarding, image generation and enhancement for attractive, personalized recommendations, and other applications to automate and process the huge amounts of visual data in Rakuten’s businesses.  

We are looking for 3-month internship students with a background in the field of computer vision & machine learning. During this internship, students will be paired with a mentor and work on one of our relevant research topics. 

The Vision Program, which is one of our core science programs, aims to process and understand images and videos using advanced computer vision and machine learning. We are working areas such as estimating product attributes for large-scale image datasets for creating catalogs, robust document image understanding for smooth customer onboarding, image generation and enhancement for attractive, personalized recommendations, and other applications to handle the huge amounts of visual data in Rakuten’s businesses. We are interested in leveraging weakly-, semi-, and unsupervised techniques to reduce labeling costs as well as techniques to increase efficiency during model training and deployment.

The Vision Program is looking for a research scientist with strong research background in the field of computer vision & machine learning and motivation to join our team.

The Vision Program, which is one of our core science programs, aims to process and understand images and videos using advanced computer vision and machine learning. We are working in areas such as estimating product attributes for large-scale image datasets for creating catalogs, robust document image understanding for smooth customer onboarding, image generation and enhancement for attractive, personalized recommendations, and other applications to handle the large amount of visual data in Rakuten’s businesses. We are interested in leveraging weakly-, semi-, and unsupervised techniques to reduce labeling costs as well as techniques to increase efficiency during model training and deployment.

The Vision Program is looking for a research scientist with strong research background in the field of computer vision & machine learning and motivation to join our team.

The team carries out applied research in the speech processing areas primarily in speech recognition and synthesis, keyword spotting, speaker verification, speech diarization, source-channel separation, and other potential areas involving speech and/or audio signals that would support a wide range of applications within the Rakuten ecosystem and its businesses. Some examples of business use cases include Interactive Voice Response (IVR) systems and automatic transcription of customer engagements for quality enhancement purposes, to name a few.

Within the Voice program at RIT, we are looking for researchers who are deeply passionate about Machine Learning / Deep Learning and aspire to apply it to building new experiences in the space of Conversational AI.

As a research scientist, you will get an opportunity to work on hard technical problems in the areas of natural language understanding (NLU), natural language generation (NLG), question-answering,

chatbots, and multi-turn dialog systems for multi-language settings (especially, Japanese and English). Along with a team of skilled ML scientists, you will be expected to work on large-scale data, formulate scientific hypotheses, experiment to validate the ideas and make a research impact.

Within the Voice program at RIT, we are looking for researchers who are deeply passionate about Machine Learning / Deep Learning and aspire to apply it to building new experiences in the space of Conversational AI.

As a research scientist, you will get an opportunity to work on hard technical problems in the areas of natural language understanding (NLU), natural language generation (NLG), question-answering,

chatbots, and multi-turn dialog systems for multi-language settings (especially, Japanese and English). Along with a team of skilled ML scientists, you will be expected to work on large-scale data, formulate scientific hypotheses, experiment to validate the ideas and make a research impact.

The team carries out applied research in the speech processing areas primarily in speech recognition and synthesis, keyword spotting, speaker verification, speech dialization,

source-channel separation, and other potential areas involving speech and/or audio signals that would support a wide range of applications within the Rakuten ecosystem and its businesses.

Some examples of business use cases include Interactive Voice Response (IVR) systems and automatic transcription of customer engagements for quality enhancement purposes, to name a few.

The team carries out applied research in the speech processing areas primarily in speech recognition and synthesis, keyword spotting, speaker verification, speech dialization, source-channel separation,

and other potential areas involving speech and/or audio signals that would support a wide range of applications within the Rakuten ecosystem and its businesses.

Some examples of business use cases include Interactive Voice Response (IVR) systems and automatic transcription of customer engagements for quality enhancement purposes, to name a few.

We are looking for a Vice-Group manager of Product Sciences team to scale Rakuten Membership Services and Data Sciences engineering and technology.

The Vice-Group manager of Product Sciences will be responsible for both the technical direction and growth of community of engineers and data scientists.

The Customer Program, one of our core sciences programs, builds models for customer understanding and applying them to customer targeting solutions. We are working on the following research topics in the Customer Program at our Tokyo office:

Credit Scoring: It is one of crucial research topics across Rakuten Group (Mainly in our fintech businesses such as credit card, payment, securities and banking business). Research scientist applies machine learning, data mining and optimization techniques to solve challenging problems, build models and solutions regarding credit scoring related projects. Our solution includes scoring for both business clients and individual users.

Social Graph: Rakuten has already been applying various kinds of marketing measures based on customer profiling, target prospecting algorithms. Those models were built with individual customer behavior data. However, if we aware of social relationship between Rakuten members, such as family, colleges, friends, it is possible to improve performance of the marketing measures. With this assumption, research scientists builds the social graph structure among Rakuten members and their relatives by collecting data from multiple Rakuten services, and apply graph algorithms including graph neural networks to conduct marketing measures, such as family introduction campaign, based on the social graph.

Geo Science: With more than 70 different services in the Rakuten Group globally, our data science teams cover diverse initiatives in both the online and offline worlds. Offline Geo data and Geo Science applications are key to the optimization and overall success in fields including Logistics, Site Planning, Area Marketing and improving Customer Experience. The Geo Science Team creates algorithms and builds new Geo Intelligence layers to better understand offline activity to support many Rakuten Group services.

Marketing Optimization: Rakuten group is contacting to our customers with various kinds of communication channels such as showing banners on web pages, sending email, pushing notifications through apps to promote our products and services. To deliver right contents at the right time to the right customers, research scientist applies various kinds of machine learning algorithms such as reinforcement learning, deep learning to build models to marketing platforms.

Federated Learning: Rakuten group is conglomerate.  As each group company has its own privacy policy, detailed user behavior data cannot be shared with each other even if they are within Rakuten Group. Federated learning scheme is required in order to build customer models with fully utilizing Rakuten group data and complying privacy policies.

Fintech Optimization: Rakuten Securities is one of the top asset management platforms in Japan. Our scientists work closely with foreign exchange dealers and stock traders from Rakuten Securities to develop algorithms that competitively price assets based on real-time customer transactions and market conditions. We are working on challenging problems including pricing and user demand forecasting, and pricing optimization for services including foreign exchange and stock lending, using various machine learning algorithms.

We are looking for research scientists with a strong research background in user behavior, machine learning, and a strong desire to join our team.

Customer Program, which is one of our core science program, aims to build universal customer models and its applications. We’re working on following four research topics in Customer Program at Tokyo office. 

Customer Pre-trained Model: Research scientists try to build universal customer models and its applications, motivated by the recent success of pre-trained models in language and vision fields. Such models can be adapted to improve diverse set of solutions for services including recommendation systems, customer targeting solutions and credit scoring functions to name a few. As initial and simpler applications, we work on predictions of customer features so-called customer DNA.

Credit Scoring: It is one of crucial research topics across Rakuten Group (Mainly in our fintech businesses such as credit card, payment, securities and banking business). Research scientist applies machine learning, data mining and optimization techniques to solve challenging problems, build models and solutions regarding credit scoring related projects. Our solution includes scoring for both business clients and individual users.

Policy Optimization: Rakuten group is contacting to our customers with various kinds of communication channels, such as showing banners on web pages, sending email, pushing notifications through apps, to promote our products and services. In order to deliver right contents at the right time to the right customers, research scientist applies reinforcement learning algorithms to build models to contents delivery platforms. 

CDNA-IA: Customer DNA (CDNA) is a platform to manage Rakuten customer profiles by utilizing data from different Rakuten businesses. CDNA-IAs, a part of CDNA, are predicted customer profiles using machine learning profiles. The team builds and operates many models for CDNA-IAs. The team does apply recent research progress to develop efficient data pipeline and model creation.

The Customer Program, one of our core sciences programs, builds models for customer understanding and applying them to customer targeting solutions. We are working on the following research topics in the Customer Program at our Tokyo office:

Credit Scoring: It is one of crucial research topics across Rakuten Group (Mainly in our fintech businesses such as credit card, payment, securities and banking business). Research scientist applies machine learning, data mining and optimization techniques to solve challenging problems, build models and solutions regarding credit scoring related projects. Our solution includes scoring for both business clients and individual users.

Social Graph: Rakuten has already been applying various kinds of marketing measures based on customer profiling, target prospecting algorithms. Those models were built with individual customer behavior data. However, if we aware of social relationship between Rakuten members, such as family, colleges, friends, it is possible to improve performance of the marketing measures. With this assumption, research scientists builds the social graph structure among Rakuten members and their relatives by collecting data from multiple Rakuten services, and apply graph algorithms including graph neural networks to conduct marketing measures, such as family introduction campaign, based on the social graph.

Geo Science: With more than 70 different services in the Rakuten Group globally, our data science teams cover diverse initiatives in both the online and offline worlds. Offline Geo data and Geo Science applications are key to the optimization and overall success in fields including Logistics, Site Planning, Area Marketing and improving Customer Experience. The Geo Science Team creates algorithms and builds new Geo Intelligence layers to better understand offline activity to support many Rakuten Group services.

Marketing Optimization: Rakuten group is contacting to our customers with various kinds of communication channels such as showing banners on web pages, sending email, pushing notifications through apps to promote our products and services. To deliver right contents at the right time to the right customers, research scientist applies various kinds of machine learning algorithms such as reinforcement learning, deep learning to build models to marketing platforms.

Federated Learning: Rakuten group is conglomerate.  As each group company has its own privacy policy, detailed user behavior data cannot be shared with each other even if they are within Rakuten Group. Federated learning scheme is required in order to build customer models with fully utilizing Rakuten group data and complying privacy policies.

Fintech Optimization: Rakuten Securities is one of the top asset management platforms in Japan. Our scientists work closely with foreign exchange dealers and stock traders from Rakuten Securities to develop algorithms that competitively price assets based on real-time customer transactions and market conditions. We are working on challenging problems including pricing and user demand forecasting, and pricing optimization for services including foreign exchange and stock lending, using various machine learning algorithms.

We are looking for research scientists with a strong research background in user behavior, machine learning, and a strong desire to join our team.

RIT (Rakuten Institute of Technology) is an R&D organization working on core research areas to contribute to Rakuten Business. RIT follows AIPPM (AI Platform Program Management) strategies to align research with Rakuten Business.

TEG (Tokyo Engineering Group) works closely with RIT scientists to build production-adoptable AI prototypes and accelerate research in production. Flexibility to adopt to technology and business needs to build faster PoCs (Proof of Concepts) is essential.

RIT (Rakuten Institute of Technology) is an R&D organisation working on core research areas to contribute to Rakuten Business. RIT follows AIPPM (AI Platform Program Management) strategies to align research with Rakuten Business. TEG (Tokyo Engineering Group) works closely with RIT researchers to build production-adoptable AI prototypes and accelerate research in production.

Rakuten Institute of Technology Tokyo, located at the Rakuten headquarters of Rakuten, was established in 2005. Rakuten Institute of Technology Tokyo covers various research areas including NLP, Advanced Analytics, computer vision, HCI and many more. We focus on applying advanced machine learning and state-of-the-art deep learning to many fields. Please refer following URL for more details.

Operations Management Group as the name refer manages all operation of RIT and optimize workflow so that all members enable to focus on the research work. You will manage all of operation work such as general operations, PR and data utilization.

We are looking for a vice-section manager for RIT Tokyo 2 Section to scale Rakuten Membership Services and Data Sciences engineering and technology.

The Language program, which is one of our core science programs, aims to automatically create a well-organized product catalog using advanced NLP and ML technologies to generate structured information from unstructured product titles, descriptions, and images. We are also interested in leveraging weakly/semi-supervised and unsupervised techniques to incorporate signals beyond traditional sources, such as customer behavior data, to augment our language understanding. We are looking for research scientists with a strong research background in the NLP field and strong motivation to join the Language program.

The Language program, which is one of our core science programs, aims to automatically create a well-organized product catalog using advanced NLP and ML technologies to generate structured information from unstructured product titles, descriptions, and images. We are also interested in leveraging weakly/semi-supervised and unsupervised techniques to incorporate signals beyond traditional sources, such as customer behavior data, to augment our language understanding. We are looking for research scientists with a strong research background in the NLP field and strong motivation to join the Language program.

The Language program, which is one of our core science programs, aims to automatically create a well-organized product catalog using advanced NLP and ML technologies to generate structured information from unstructured product titles, descriptions, and images. We are also interested in leveraging weakly/semi-supervised and unsupervised techniques to incorporate signals beyond traditional sources, such as customer behavior data, to augment our language understanding. We are looking for research scientists with a strong research background in the NLP field and strong motivation to join the Language program.