Senior ML Engineer, Personalization

Role information

### Role Overview As an ML Engineer on the Personalization team, you will own the end-to-end lifecycle of recommendation models. This includes feature engineering, model selection, and the deployment of inference services. You will be responsible for moving prototypes from research into production environments while ensuring high availability and low latency. ### Key Responsibilities - Design and implement deep learning models for candidate retrieval and ranking using PyTorch or TensorFlow. - Optimize data pipelines using Spark to process terabytes of user interaction data. - Conduct A/B tests to validate model performance against business KPIs such as Click-Through Rate (CTR) and session length. - Collaborate with the Infrastructure team to scale model serving via Kubernetes and FastAPI. ### Qualifications - 3+ years of professional experience in machine learning engineering. - Strong proficiency in Python and SQL, with a deep understanding of vector databases (e.g., Pinecone, Milvus). - Experience deploying models into a production environment using CI/CD pipelines. - Master's or PhD in Computer Science, Statistics, or a related quantitative field is preferred.