ML Engineer - Production Pipelines for D2C (Hybrid Remote)

PlayStation; Zaventem; Hybrid; EUR 60,000 - 100,000; Full time

Job summary

Sony Interactive Entertainment (PlayStation) is seeking a hands-on ML Engineering role within the D2C Data Science group. You will build and operate the in-house ML platform used by data scientists to design, deploy, monitor, and experiment with models powering production decisions across the D2C portfolio.

The role involves developing services and pipelines for model training, scoring, and monitoring, with a strong emphasis on production quality, testing, and cross-functional collaboration with

Qualifications

• Master’s degree in Computer Science, ML or related field with 3+ years of relevant experience.
• Strong software engineering foundation with Python or Java production systems, including monitoring and debugging.
• Experience using SQL to prepare and investigate data in production environments.
• Ability to work with data scientists and product partners across a globally distributed team.
• Familiarity with AI-assisted development tools and ML workflows.

Responsibilities

• Contribute to services and pipelines for model training, batch or real-time scoring, and production monitoring.
• Develop data transformations, feature aggregations and integrations using Python or Java and SQL.
• Help integrate model deployments and A/B experiments with data scientists and other ML engineers.
• Improve automated testing, deployment, monitoring and troubleshooting; participate in on-call rotation (no nights).

Skills

Python

Java

SQL

Machine Learning

Software Engineering

Education

Master’s degree in Computer Science / ML

Tools

Python

Java

SQL

Job description

Sony Interactive Entertainment (PlayStation) is seeking a hands-on ML Engineering role within the D2C Data Science group. You will build and operate the in-house ML platform used by data scientists to design, deploy, monitor, and experiment with models powering production decisions across the D2C portfolio.

The role involves developing services and pipelines for model training, scoring, and monitoring, with a strong emphasis on production quality, testing, and cross-functional collaboration with