Machine Learning Engineer - Recommendation Department, AI & Data Division

Job Description:

Department Overview
Shape the Future of Rakuten's Users Experience through recommendation.
Recommendation Department is seeking a passionate Machine Learning Engineer to join our team based in Tokyo. The team, in the core of Rakuten’s AI division, holds a platform powering personalized recommendation experiences across Rakuten's diverse ecosystem of services through state-of-the-art AI algorithms, impacting millions of users daily. We are looking for a talented machine learning engineer with a passion for recommendation systems and a proven track record of delivering impactful works.

Our mission is to think "Recommendation" as a product to maximize user’s satisfaction through content personalization and discovery.
We support business growth by impacting millions of users, bridging user needs and technology leveraged by AI. We work closely with software and AI engineers in order to deliver the best solutions to our users.
We work with Agile, are passionate about our product and its continuous improvement through AI.

Position:

Position Details
As a Machine Learning Engineer, you will help build and scale Rakuten’s recommendation and personalization systems by contributing to the development and deployment of ML solutions into production. You will support the design and implementation of end-to-end model and data pipelines (feature creation, training, evaluation, and serving), working closely with senior engineers and cross-functional partners to deliver reliable, measurable improvements across Rakuten’s business units. In this role, you’ll apply experimentation, monitoring, and modern ML engineering best practices to improve model quality and system performance to ship maintainable, scalable, and user-impacting recommendation experiences that drive engagement and business growth.

Your Mission
- Build & Scale ML Pipelines: Design, develop, and maintain robust end-to-end ML pipelines for Rakuten’s recommendation and personalization platform - covering data generation, training, evaluation, deployment - with a focus on reliability, scalability, and maintainability.
- Deep Dive into Models & Data: Develop a strong understanding of data sources, feature definitions, and model behavior to identify gaps in data quality, feature coverage, online/offline consistency, and system latency - and implement improvements that better align platform performance with business needs.
- Enable Business Value through ML: Deliver production-grade models and reusable ML components (e.g., feature sets, embeddings, ranking services), with clear documentation and measurable outcomes, to accelerate adoption and business impact across multiple Rakuten business units.
- Collaborate Across Teams: Partner closely with Business Units (BUs), data scientists, data engineers, analytics, and product teams to define use cases, translate requirements into ML solutions, and deliver effective data pipelines and deployable models.
- Plan and Deliver: Prioritize and manage ML engineering work across new development and operations (e.g., model retrains, monitoring/alerting, incident response, backfills, A/B test support, and production releases) to meet platform SLAs and ensure continuous improvement.

Work Environment
- AI Product: Recommendation and Personalization (Recsys).
- Cross-border team JP-EU (Japan - France/Spain/UK).
- International team culture, working with cross-cultural and cross-functional teams.

Mandatory Qualifications:
- Bachelor’s degree or higher in Computer Science, Information Technology, Engineering, or a related field.
- More than 3 years of hands-on experience in Machine Learning Engineering / Applied ML / MLOps (or closely related software engineering roles), including building, deploying, and operating ML pipelines in production.
- Demonstrated experience with ML fundamentals and workflows, such as feature engineering, model training/evaluation, and production inference (batch and/or real-time).
- Excellent communication skills, with the ability to clearly explain technical concepts and trade-offs to both technical and non-technical stakeholders.
- Proven ability to work effectively in a collaborative, cross-functional environment (e.g., partnering with software engineers, data scientists, data engineers, analysts, and product managers).

Desired Qualifications:
- Experience building and operating ML pipelines (data/feature processing, training, evaluation, deployment, and monitoring) using frameworks such as scikit-learn, PyTorch, in cloud and/or on-premises environments.
- Exposure to MLOps best practices (e.g., CI/CD for ML, model registry, reproducible training, model/feature monitoring, and production incident troubleshooting).
- Experience collaborating with cross-functional and cross-border teams, effectively aligning with stakeholders across engineering, product, and analytics.
- Experience with experiment design and A/B testing, and strong analytical skills using tools such as SQL, Spark, and Python to evaluate model impact and iterate based on results.
- Language: English as the primary working language (professional proficiency required); Japanese is a plus.

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