Rakuten, Inc. is one of the world’s leading Japanese tech conglomerates, providing a variety of services including e-commerce, travel booking, telecommunications, credit card, e-money, portal & media, digital marketing, professional sports, and others (http://global.rakuten.com/corp/about/).
Rakuten has operations in Asia, Western Europe, and the Americas.
Rakuten Institute of Technology (RIT) is Rakuten’s global data innovation group focused on data R&D and strategic technology development for portfolio companies. With offices in Tokyo, Singapore, Bangalore, Boston, and Paris, RIT’s contributions cover a broad range of topics, including natural language processing, computer vision, customer analytics, and more.
The current opportunity is with the Rakuten Translate team within RIT Singapore. The team’s mission is to help Rakuten strengthen its global presence and lower language barriers by developing language translation solutions for Rakuten’s portfolio businesses.
We publish in top-tier conferences while not shying away from tackling engineering challenges when deploying large-scale, user-facing systems in collaboration with our engineering teams. By joining our team, you will pursue cutting edge research coupled with opportunities to have immediate impact on the business. You will play an active role in shaping research and product roadmap, and collaborate closely with research, engineering, and business teams globally.
Contribute to development of language translation solutions
Deliver results by developing new technologies that improve business metrics
Be a thought-leader, keeping up with the academic and industry trends
Demonstrate long-term vision, while effectively supporting short-term goals
Rakuten Asia Pte Ltd
PhD or Masters with 2+ years of R&D experience in industry or research institute
Strong machine learning fundamentals
Passion for working with both words and numbers
Demonstrated interest in Machine Translation technologies
Publications in top-tier conferences and journals
Not required but very nice to have is experience with large-scale model deployment
Level of experience (years):
Mid Career (2+ years of experience)