Das ist der Job
Responsibilities Design, build, and maintain production recommendation systems at consumer scale.
Darum lohnt es sich
Collaborate with client stakeholders and engineering teams to deliver scalable ML solutions. Develop ranking and retrieval models to improve personalization and user experience. Own ML pipelines end-to-end, from feature engineering and training to deployment and monitoring.
Analyze large-scale user behavior data and run A/B tests to optimize recommendation performance. Improve reliability, scalability, and latency of production ML systems on AWS GCP Azure.
Requirements 6 years of experience in Software/Data Engineering or ML roles. 4+ years building and operating ML systems in production. 2-3+ years working on recommendation systems, ranking, retrieval, or personalization. Strong Python and SQL skills. Experience with PyTorch or TensorFlow. Experience deploying ML systems on AWS, GCP, or Azure.
Experience working with large-scale consumer data and production environments. Advanced English level and ability to work autonomously with stakeholders. Experience in e-commerce, adtech, martech, or consumer products. Experience with A/B testing, feature stores, Spark, or low-latency inference systems. #J-18808-Ljbffr
