Staff ML Engineer, Search Ads Shopping Relevance Models

Job description

Collaborate on user journey understanding, metric and label formulation, feature and model improvements, live traffic experiments, data analysis, tools and infrastructure, and more, to predict and improve user experience on search ads. Train machine learning models and explore model features, architectures, and hyperparameters in order to continuously improve model accuracy, in particular Gemini model(s). Own and lead efforts to push our modeling, data or serving to new dimensions. Implement code and tests for model training (Python, C++, e.g. on top of REX) and backend code and tests for model serving (in C++) and logging. Run ads experiments and develop experiment metrics, if needed. Interpret, understand, and integrate a wide variety of metrics related to user experience and quality. Perform model, experiment, and human evaluation analyses including writing custom analysis tools if needed. Minimum Qualifications: Bachelor’s degree or equivalent practical experience. 8 years of experience programming in Python or C++. 5 years of experience testing, and launching software products. 5 years of experience leading ML design and optimizing ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning). 5 years of experience with one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field. 3 years of experience with software design and architecture. Preferred Qualifications: Master’s degree or PhD in Engineering, Computer Science, or a related technical field. 8 years of experience with data structures and algorithms. 3 years of experience in a technical leadership role leading project teams and setting technical direction. 3 years of experience working in an organization involving cross-functional, or cross-business projects. Knowledge of modern machine learning techniques including deep learning and LLMs. Knowledge of statistics and experiment design.