Description
Situated in the heart of Singapore's Central Business District, Rakuten Asia Pte. Ltd. is Rakuten's Asia Regional headquarters. Established in August 2012 as part of Rakuten's global expansion strategy, Rakuten Asia comprises various businesses that provide essential value-added services to Rakuten's global ecosystem. Through advertisement product development, product strategy, and data management, among others, Rakuten Asia is strengthening Rakuten Group's core competencies to take the lead in an increasingly digitalized world.
Rakuten Group, Inc. is a global leader in internet services that empower individuals, communities, businesses, and society. Founded in Tokyo in 1997 as an online marketplace, Rakuten has expanded to offer services in e-commerce, fintech, digital content, and communications to approximately 1.7 billion members around the world. The Rakuten Group has nearly 32,000 employees and operates in 30 countries and regions. For more information visit https://global.rakuten.com/corp/.
The Frontier Research Department is responsible for advancing Rakuten’s core AI capabilities, including large language models and related technologies. The team works across model development, post-training, evaluation, deployment, and data strategy to improve model quality, alignment, scalability, and practical effectiveness. This role sits within the Frontier Research Department and works closely with researchers, evaluation teams, model engineers, and GPU infrastructure teams to strengthen the performance and readiness of Rakuten’s self-developed LLMs.
Responsibilities
Support requirement analysis and strategy optimization for Rakuten’s Large Language Models, with a focus on data quality, alignment, model effectiveness, and deployment readiness.
Collaborate with researchers, model engineers, and evaluation teams to identify model weaknesses and define improvement opportunities.
Define data requirements for post-training, alignment, and evaluation across priority domains and use cases.
Develop and improve methodologies for data acquisition, data production, and data quality management, while balancing quality, scalability, and cost.
Evaluate the effectiveness of data production tools and workflows and partner with technical teams to improve them.
Work with GPU and infrastructure teams to support model deployment planning, resource prioritisation, and efficient production rollout of Rakuten’s models.
Contribute to model pricing and cost strategy by assessing trade-offs across model quality, serving cost, infrastructure usage, and business requirements.
Translate ambiguous model quality, deployment, and cost challenges into structured problem statements, prioritised workstreams, and measurable outcomes.
Required Qualifications
5+ years of experience in product management, machine learning products, AI/ML platform strategy, or related technical program roles.
Solid understanding of LLM alignment and post-training approaches such as SFT, preference optimization, or RLHF.
Experience working with data quality, evaluation, or model improvement workflows for ML/AI systems.