Design and implement ML models to improve predictions of user interaction, click-through rate (CTR), and conversion rate (CVR) Develop and optimize retrieval algorithms, leveraging techniques from classical IR and modern deep learning Contribute to core modeling areas such as deep neural networks, contextual bandits, multi-task learning, and LLM-based ranking signals Work with large-scale, distributed datasets to identify new signals and improve model accuracy and robustness Collaborate with cross-functional teams across engineering, infrastructure, and product to scale models to production Participate in designing and running large-scale experiments to validate new model architectures and learning strategies8+ years of experience applying machine learning and statistical modeling at scale, preferably in ad tech, recommender systems, or web-scale search/retrieval Deep experience with neural network architectures (e.g., Transformers, DNNs, RNNs) and training pipelines using TensorFlow, PyTorch Practical understanding of reinforcement learning, explore/exploit strategies, and bandit-based optimization Experience working with high-volume data pipelines, A/B testing infrastructure, and performance measurement at scale Proficient in Python and familiar with SQL, Scala, or Java for production environments Ability to translate abstract ideas into concrete, high-impact solutions Bachelors, or equivalent experience, in Computer Science, Machine Learning, Artificial Intelligence, Information Retrieval, or a related field. Everything we do is designed for trust, connection, and impact: We respect user privacy, integrate advertising thoughtfully into the experience, and deliver value for advertisers of all sizes-from small app developers to big, global brands.