Lead end-to-end machine learning initiatives focused on improving player engagement and retention, from initial concept through production deployment.
Build scalable, reusable machine learning pipelines with a focus on reliability, maintainability, and performance.
Design and manage CI/CD workflows for machine learning using tools like MLflow, Jenkins, and GitOps to enable automated and efficient model deployment.
Monitor model performance in production, implementing retraining strategies, drift detection, and continuous optimization.
Partner with cross-functional teams to translate business goals and user insights into high-impact machine learning solutions.
Mentor other engineers and help define best practices for machine learning system design, development, and deployment.

Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a related technical field
At least 3 years of experience working with machine learning systems in production environments
Strong proficiency in Python and SQL, with experience working on distributed data platforms such as Spark
Proven experience delivering production-grade machine learning models that drive measurable business impact
Hands-on experience with Databricks for managing machine learning workflows, model lifecycle, and collaborative development
Experience designing experiments and analyzing A/B tests to validate and optimize model performance
Strong communication and collaboration skills, with experience mentoring or leading technical initiatives.

Senior Machine Learning Engineer, Personalization

Key skills