Electronic Arts - Senior Machine Learning Engineer

1. Bachelor's degree in Computer Science, Data Science, or related field.
2. Comprehensive knowledge in Machine Learning, MLOps practices, and ML model production.
3. Excellent communication and coordination skills to work in a cross-functional team
4. Profound knowledge in both Machine Learning and MLOps.
5. Experience in designing and maintaining ML systems, developing production-ready ML models.
6. Knowledge of the latest technology and platforms in the field such as AWS SageMaker, GCP Vertex AI, Airflow, Docker, CI/CD practices, ...
7. Comfortable working in an agile environment.

We are a global team of creators, storytellers, technologists, experience originators, innovators and so much more. We believe amazing games and experiences start with teams as diverse as the players and communities we serve. At Electronic Arts, the only limit is your imagination.

The Data & Analytics (DnA) function brings both quantitative and qualitative insights to the entire company (including all game teams) through Studio Analytics, Data Engineering, Data Science and Machine Learning Engineering. We develop and improve our Studio’s data technical capabilities to bring data insights right to the studio partners' fingertips.

EA serves millions of players with multi-genre online experiences via Live Services. Games of the last decade continue to entertain and engage audiences with new titles launching each year. The common need across all game experiences is modern personalization approaches that enhance and optimize player experiences, leveraging the latest Machine Learning technologies to retrofit and augment game features, game systems, and game mechanics. We are looking for a Machine Learning engineer who will work amidst unique technologies to deliver in-game Personalization by building global-scale production systems. You will report to the technical director of AI/ML. If you have a passion for creating advanced analytics data products to allow partners to make crucial decisions, you want to make impacts to help player engagement by providing an autonomous platform; then we want to talk to you.

Responsibilities:

1. Enforce the application of best practices in MLOps and ML model production.
2. Optimize production-ready ML models.
3. Design, construct, and maintain production ML systems.
4. Work closely with team members for efficient deployment and scalability of ML models.
5. Stay updated with the latest technology and platforms and guide the MLE team in the adoption of new technologies and platforms.