Applied Scientist - AI Experiences Search

Own end-to-end applied research and engineering to improve web data quality, scale, and model readiness for Search, grounding, and LLM training. Develop and apply state-of-the-art machine learning, LLM, statistical, and data mining techniques to solve complex web-scale problems and advance platform capabilities. Analyze large-scale web data, formulate hypotheses, and design rigorous experiments that produce trustworthy, actionable insights and measurable impact. Apply LLMs and prompt engineering to score, classify, label, and evaluate data, enabling scalable model training, evaluation, and continuous improvement. Translate complex technical problems and research findings into product decisions, technical strategies, and engineering roadmap in partnership with stakeholders. Bachelor's 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 Master's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) 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. 1+ years of experience contributing to research publications, patents, or peer-reviewed academic papers. 1+ years of experience in product development in machine learning and related areas. Hands-on experience developing algorithms and models using deep learning frameworks such as TensorFlow and PyTorchHands-on experience with large language models (LLMs), such as GPT, BERT, or similar architectures, with an understanding of their capabilities and limitations. Knowledge of natural language processing (NLP), including tokenization, semantic understanding, representation learning, classification, and text generation. Understanding of modern machine learning and deep learning techniques, with hands-on experience using architectures such as Transformers/Attention, DNNs, CNNs, or RNNs and frameworks such as PyTorch, TensorFlow, or Keras. Algorithmic, statistical, and analytical skills, with demonstrated ability to apply advanced techniques to complex, real-world problems. Ability to translate technical vision into experimentation, system design, implementation, and measurable results, working both independently and collaboratively across teams. Experience with large-scale or distributed data processing, high-performance computing, stream processing, or SCOPE is preferred. Self-motivated and adaptable, with strong communication and cross-team collaboration skills and the ability to operate effectively amid evolving priorities.