Machine Learning Engineer

re-zoo-me; Singapore; Hybrid; SGD 120,000 - 180,000; Full time

Job summary

re-zoo-me is seeking a data-driven ML engineer to join our team in Singapore. You will develop, productionise and optimise models for recommendations, personalization and user behaviour, with end-to-end ML pipelines and monitoring.

You will collaborate with Data Scientists and Data Engineers to turn prototypes into reliable production systems and continuously improve deployed solutions in a data-intensive environment.

Qualifications

• Bachelor's degree or above in a related field.
• 3+ years building recommendation systems or user behaviour models.
• Proficient in Python and software engineering fundamentals.
• Experience with large-scale data processing using Spark, PySpark or Flink.
• Hands-on production experience with ML models.

Responsibilities

• Develop and productionise ML models for recommendations, personalization and user behaviour.
• Build scalable ML pipelines from data prep to deployment and monitoring.
• Design batch and real-time inference to meet latency and reliability goals.
• Collaborate with Data Scientists and Data Engineers to deploy production systems.
• Monitor performance and iterate to improve models and pipelines.
• Evaluate new ML and MLOps developments for business use cases.

Skills

Python

Software engineering

ML algorithms

Statistics

Feature engineering

Spark PySpark Flink

Production ML

Data analysis

Education

Bachelor's degree or above in Computer Science or related field

Tools

Spark

PySpark

Flink

Job description

About the Role

This role sits at the intersection of machine learning, big data engineering, and data science. You will work with large-scale datasets to develop predictive models, recommendation solutions, and intelligent systems that support product performance, user engagement, and business decision-making. You will be involved throughout the ML lifecycle—from data exploration and feature engineering to model development, evaluation, deployment, and continuous optimisation.

Key Responsibilities

• Develop, productionise and maintain machine learning models for recommendation, personalisation, user behaviour, prediction, classification and other data-driven applications.
• Build scalable ML pipelines covering data preparation, feature engineering, model training, evaluation, deployment and monitoring.
• Design and optimise batch and/or real-time model inference solutions for reliability, scalability, latency and production performance.
• Work with large volumes of structured and unstructured data to develop effective machine learning solutions.
• Collaborate closely with Data Scientists and Data Engineers to transform ML prototypes and data pipelines into reliable production systems.
• Monitor model and system performance, identify degradation or operational issues, and continuously improve deployed solutions.
• Contribute to ML engineering practices, including testing, versioning, CI/CD, reproducibility and model lifecycle management.
• Evaluate new developments in machine learning, MLOps and AI and apply relevant technologies to business and product use cases.

Requirements

• Bachelor's degree or above in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
• 3+ years of relevant experience in building recommendation systems, ranking models, personalisation, or user behaviour modelling.
• Strong programming skills in Python and good software engineering fundamentals.
• Strong understanding of machine learning algorithms, statistics, model evaluation, and feature engineering.
• Experience working with large-scale datasets using Spark, PySpark, Flink or other distributed processing technologies.
• Hands-on experience building or deploying ML models in production environments.
• Strong analytical and problem-solving skills, with the ability to translate business problems into data and machine learning solutions.

Nice to Have

• Experience in gaming, e-commerce, fintech, advertising, or other data-intensive industries.
• Familiarity with cloud platforms such as AWS, Azure, or GCP is an advantage.