Senior Machine Learning Engineer

You will work at the intersection of ML model development, production engineering, and data-driven experimentation , collaborating with cross-functional teams to ensure scalable, performant, and personalized experiences. This role is ideal for engineers who have built and iterated on production-grade personalization systems and thrive on both deep technical challenges and business impact. A SNAPSHOT OF YOUR RESPONSIBILITIES Design and build scalable recommendation and personalization models (ranking, re-ranking, user embeddings, semantic retrieval) Own the full model lifecycle: from data preparation , training , and evaluation , to versioning , deployment , and monitoring Develop and maintain continuous training loops and model refresh strategies for dynamic personalization Set up and interpret A/B experiments to optimize model performance and user engagement Collaborate with data engineers, MLOps teams, and product managers to ensure models integrate seamlessly into real-time and batch inference pipelines Leverage platforms like Databricks, MLflow , and feature stores to streamline model experimentation and reproducibility Apply LLMs and AI agents to improve personalization workflows and accelerate ML development pipelines Contribute to architecture decisions for personalization services and model serving infrastructure Mentor and provide technical guidance to junior data scientists and ML engineers , conducting code reviews, sharing best practices, and supporting their growth in areas such as model development, experimentation, and productionization WHAT YOU WILL NEED At least 3-7 years of experience in machine learning, applied data science , or related fields, with a strong focus on recommendation systems or personalization Demonstrated experience in developing and deploying ML models into production environments Deep understanding of ranking systems, user behavior modeling , and evaluation techniques (e.g., NDCG, AUC, MAP, CTR) Proficient in Python and ML libraries like PyTorch, TensorFlow , and frameworks such as Transformers or LightGBM Experience with Databricks , Spark, or similar big data platforms for large-scale model training and data processing Familiarity with model versioning, feature stores, experiment tracking , and MLflow Strong grasp of A/B testing design , analysis, and interpreting results for iterative model improvements Experience with LLM-based pipelines , semantic search , or vector similarity systems (e.g., FAISS, Vespa) is a plus Comfort working in cloud-native environments such as AWS or GCP NICE TO HAVE, BUT NOT REQUIRED Experience using or building AI agents , LangChain , or workflow automation frameworks for model experimentation Exposure to real-time inference systems and streaming architectures (Kafka, Flink) Experience working on personalization systems at scale , particularly for high-traffic applications or live events Contributions to open-source ML tools or research in personalization-related fields