Machine Learning Engineer, Science Product Group - Measurement & Insight Section, Analytics Data Engineering Department (ADED)

Job Description:

Department Overview

Following the strategic vision “Rakuten as a data-driven membership company”, we are expanding our data activities across our multiple Rakuten group companies.

Our mission is to provide data/AI related products to users to improve marketing efficiency and effectiveness.

Join Our Innovative AI Product Team

We are an end-to-end AI product team within the Science Product Group, comprised of Data Scientists, Machine Learning Engineers, and Product Managers. We foster a "missionary" culture, empowering team members to fully own their product responsibilities and develop hybrid skillsets. This means Data Scientists can expand their engineering and product expertise, while Machine Learning Engineers can engage in scientific research and gain product acumen. Product Managers are encouraged to leverage tools like Python to build prototypes and deepen their understanding of scientific solutions and product engineering systems.

We offer the unique opportunity to work on solutions for both internal and external Rakuten businesses, spanning diverse industries and marketing areas. This provides invaluable experience across multiple domains.

Our team is highly international and operates primarily in English. We maintain a flat organizational structure, minimizing hierarchy and fostering a collaborative environment. We value continuous learning, growth, and mutual respect among team members.

Position:

Why We Hire

We are looking for an experienced Machine Learning engineer who will contribute to the development of Rakuten’s data & AI products which deliver personalized experiences (including but not limited to: business areas of marketing, advertisement, targeting campaigns, and audience expansion, etc.)

Position Details

We are on the lookout for a Machine Learning Engineer with a strong experience in software engineering to join our team. This role is designed to merge the realms of machine learning and software engineering, focusing on the development of AI-driven products at scale.

The Machine Learning Engineer will be instrumental in transforming prototypes into production-level solutions, developing new features for our AI-driven products, and designing robust & scalable machine learning architectures.

Title

Machine Learning Engineer (or MLOps Engineer)

Job Level

Senior (at least around 7 year+ of professional experience or the equivalent skills)

Work Environment

Our Tech Stack

While we advocate for using the right technology for the right task, we often leverage the following technologies

Python, Django, Flask, Golang, REST, GraphQL, Docker, Kubernetes, Helm, Argo Workflow, Argo CD, Gitlab (CI), Sentry, Presto/Trino, Hive, Hadoop, Spark, Postgres, SQL/HQL, Kubeflow, etc.

Team

An international and diverse team with highly skilled engineers

Mandatory Qualifications:

Machine Learning / MLOps

- Educational Background: Bachelor's degree or higher (Masters or Ph.D. preferred) in Computer Science, Machine Learning, Physics, Mathematics, Statistics, or a similar quantitative field.

- High level understanding of modern MLOps trends

- High level mathematical understanding of general machine learning models

- Proficiency in using at least one of modern ML frameworks such as TensorFlow, PyTorch, or Keras, etc.

- Experience in full lifecycle in production development: from (large scale) data pipelines to model training, inference APIs (both batch and real-time), and model version control & tracking/observability, including overall solution design optimizations.

Software Engineering

- High level of familiarity with the full web stack

- Expert/Senior level in at least one of the major/modern computer languages including but not limited to Python, C/C++, Java, or Go, etc.

- Experience with modern CI/CD processes & DevOps

- Experience with Cloud Native Technologies (E.g. Docker, Kubernetes)
 

Language

- Business Level English (Japanese skill is not required at all)

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