You'll mentor other MLE's and lead an effort to build scalable end-to-end machine learning solutions for our retail customersCollaborate with other MLEs to build scalable, production-ready ML solutions, taking algorithms from initial concept through to deployment Contribute to the ongoing improvement of our ML infrastructure and tooling Engage in continuous learning and development, staying up-to-date with the latest advances in machine learning and software engineeringProficiency in one or more object-oriented programming languages such as Python, Java, C++ and experience building highly scalable distributed systems Hands-on experience with building data processing pipelines, large scale machine learning systems, and big data technologies (eg: Spark, SQL, Snowflake/Hadoop, etc) Bachelors in a quantitative field, such as Computer Science, Applied Mathematics, Statistics, or Bachelors degree in quantitative field with a focus on AI in courseworkUnderstanding of machine learning model lifecycle from prototyping, feature engineering, training, inference, deployment, monitoring and continuous improvements via deep analysis) Experience in Recommender Systems, Personalization, Search, Computational Advertising or Natural Language Processing including RAG based Generative AI and transformer architecture Experience using Deep Learning, Bandits, Probabilistic Graphical Models, or Reinforcement Learning in real applications a plus Experience with Spark, TensorFlow, Keras, and PyTorch a plus Skilled in communication, problem solving, strategic thinking. To be successful, you need a strong machine learning background, proven software development skills, a love of learning, and to collaborate with cross-functional teams, including researchers, engineers, data scientists/analysts, and product managers, to develop and implement machine learning algorithms.