Senior Software Development Engineer in Test

PlayStation Global•San Mateo, CA

2d•$215,601 - $274,600•Hybrid

About The Position

Sony Interactive Entertainment LLC seeks a Senior Software Development Engineer in Test in San Mateo, CA to design, develop, and maintain automated testing frameworks and tools for PlayStation console and platform applications using both internal and external Python-based technologies.

Requirements

Master’s degree in Computer Science, Computer Engineering, or related field or equivalent
Two (2) years of experience in containerizing ML services and exploring Kubernetes-based deployments to support scalable service infrastructure
Leveraging Amazon Web Services (AWS) technologies, including EC2, S3, and Lambda, to support model training, storage, and deployment workflows
Implementing cloud-based ML infrastructure and monitoring solutions to improve system reliability and availability
Utilizing Python and ML libraries including Scikitlearn and Pandas to build predictive models, perform data preprocessing and feature engineering, and generate analytical insights
Applying supervised learning, anomaly detection, and optimization algorithms to support system tuning and predictive analytics
Creating real-time ROI and analytics dashboards using Tableau to support data-driven decision-making
Developing and maintaining automated validation scripts using Python and PyTest to test data preprocessing pipelines, model outputs, and performance metrics, ensuring model accuracy and reliability prior to deployment
Integrating automated model validation checks into CI/CD pipelines to enable continuous regression testing of ML workflows
Designing test scenarios and validation frameworks to verify end-to-end functionality of ML-powered pricing tools, including API validation and business logic verification across web-based interfaces

Responsibilities

Design, develop, and maintain automated testing frameworks and tools for PlayStation console and platform applications using both internal and external Python-based technologies.
Containerizing ML services and exploring Kubernetes-based deployments to support scalable service infrastructure.
Leveraging Amazon Web Services (AWS) technologies, including EC2, S3, and Lambda, to support model training, storage, and deployment workflows.
Implementing cloud-based ML infrastructure and monitoring solutions to improve system reliability and availability.
Utilizing Python and ML libraries including Scikitlearn and Pandas to build predictive models, perform data preprocessing and feature engineering, and generate analytical insights.
Applying supervised learning, anomaly detection, and optimization algorithms to support system tuning and predictive analytics.
Creating real-time ROI and analytics dashboards using Tableau to support data-driven decision-making.
Developing and maintaining automated validation scripts using Python and PyTest to test data preprocessing pipelines, model outputs, and performance metrics, ensuring model accuracy and reliability prior to deployment.
Integrating automated model validation checks into CI/CD pipelines to enable continuous regression testing of ML workflows.
Designing test scenarios and validation frameworks to verify end-to-end functionality of ML-powered pricing tools, including API validation and business logic verification across web-based interfaces.