Machine Learning Staff Software Engineer, Search Personalization

Job description

Design and implement personalized user models to optimize for user happiness, including Neural Deep Retrieval Models, Deep Neural Network Ranking/Scoring models, User/Content Clustering Models, Large Language Models (LLM)-based Retrieval Augmented Generation Models, and more. Build user and content clustering models to enable core personalization and ranking use cases. Enhance model performance and personalization precision/recall through advanced modeling techniques such as transformers, distillation, reward shaping, multi-task learning, neural bandits, etc. and capabilities through feature engineering, automatic parameter tuning, label quality engineering, etc. Scale the model's applications to a multitude of modalities (content, queries, videos and notifications) and use cases (retrieval, ranking, content generation, diversification, etc.). Create next-generation realtime ML models that can capture new user interests and world trends in seconds and scale model training and serving to billions of users. Minimum Qualifications: Bachelor’s degree or equivalent practical experience. 8 years of experience in software development. 5 years of experience building and deploying recommendation systems models (retrieval, prediction, ranking, embedding) in production and experience building architecture in different modeling domains. 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 5 years of experience testing, and launching software products, and 3 years of experience with software design and architecture. Preferred Qualifications: 8 years of experience with data structures and algorithms. 6 years of ML or Quality experience working on recommendation systems. Experience in recommender systems, clustering algorithms, SQL, deep model. Experience in C++, Dremel/F1 and TensorFlow. Experience working with research. Ability to drive quality projects end-to-end from design to implementation to eventual launch.