Machine Learning Engineer, Recommendation Algorithm
SmartNews
full-time
Posted on:
Location Type: Hybrid
Location: Shibuya • Japan
Visit company websiteExplore more
About the role
- Translate business goals and product strategies into impactful machine learning solutions that power personalized experiences at scale.
- Design, experiment with, and optimize ranking strategies that directly influence user engagement, retention, and monetization across key SmartNews surfaces.
- Collaborate closely with Product, Business, and cross-functional engineering teams to identify opportunities, define success metrics, and deliver measurable improvements through data-driven experimentation.
- Continuously refine models, features, and ranking logic based on performance insights and user feedback to help evolve recommendation systems to drive sustainable business growth.
Requirements
- Business-level proficiency in Japanese, with the ability to collaborate effectively with local Product and Business stakeholders
- Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, Mathematics, or a related quantitative field.
- Strong foundation in machine learning fundamentals, including supervised learning, model evaluation, feature engineering, and basic optimization techniques.
- Proficiency in Python and experience with common ML frameworks (e.g., PyTorch, TensorFlow, or similar).
- Solid coding skills with attention to code quality, readability, and maintainability.
- Experience working with data processing tools (e.g., SQL, Pandas, Spark, or similar) and handling large-scale datasets.
- Understanding of experimentation methodologies such as A/B testing and basic statistical analysis.
- Strong problem-solving skills and the ability to translate business questions into data-driven approaches.
- Internship or project experience in recommendation systems, ranking, search, ads, or personalization-related domains (nice to have).
- Hands-on experience with large-scale data processing frameworks (e.g., Spark) or distributed training environments (nice to have).
- Familiarity with deep learning models commonly used in recommendation systems (e.g., embeddings, sequence models, multi-task learning) (nice to have).
- Exposure to online serving, model deployment, or production ML workflows (e.g., feature pipelines, monitoring, model iteration) (nice to have).
Benefits
- All healthcare and social insurance required by the Japanese labor law, plus annual health check
- Visa sponsorship and overseas relocation support available for eligible candidates
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningsupervised learningmodel evaluationfeature engineeringoptimization techniquesPythonA/B testingstatistical analysisrecommendation systemsdata processing
Soft Skills
problem-solvingcollaborationcommunication