Job Description

Drive AI innovation: Lead the development of cutting-edge models that select and rank ads, predict user interaction, and optimize advertiser outcomes. You will leverage and advance Deep Learning, Reinforcement Learning, Causal Inference, and other techniques to solve complex problems. Optimize at scale: Design, build, and deploy models that operate at web scale, ensuring they are robust, scalable, and high performing in real-world settings. You will directly improve user engagement, ad relevance, and advertiser return on. Perform large-scale online and offline experiments to continuously optimize and validate model performance, ensuring real-time impact on user and advertiser experiences. Bachelor's & Masters Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 2+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field OR equivalent experience. Experience with large-scale models and deep learning algorithms and frameworks such as PyTorch. Experience building, deploying, and optimizing large-scale AI/ML models in real-world applications, especially in NLP, Information Retrieval, or Computer Vision. Publications in top-tier conferences like NeurIPS, ICML, CVPR, SIGIR, KDD, ACL, EMNLP, ICLR, WWW, WSDM or similar, demonstrating expertise in advancing the field. Experience in online advertising, search engines, or recommendation systems and game theory and mechanism design. PhD in Machine Learning, AI, or related fields. Experience working with large language models / multi-billion parameter models, focusing on their efficient training and online inference. Background in developing or modifying deep learning algorithms/architectures to improve computational and memory efficiency.

Skills Required