Senior Applied Scientist

Improvements are measured offline on relevance, accuracy, citation quality and speed, then confirmed in online experiments before they ship. Advance how AI systems search for information: understanding what a person needs, turning requests into precise queries, choosing where to look, and recognizing when enough evidence has been found. Improve the quality, coverage and reliability of grounding data, so answers are relevant, accurate and supported by the sources they cite. Train and fine-tune language models for search and grounding tasks, using supervised fine-tuning and reinforcement learning to improve both quality and speed. Design evaluation methods and reward signals that reflect what users value, such as relevance, accuracy, trustworthy citations and efficiency. Build training and evaluation data in privacy-preserving ways, including synthetic data generation. Define offline and online metrics for search and answer quality, and make sure offline gains predict what people experience in production. Dig into real-world failures, find root causes and turn them into model, data and system fixes that ship. Partner with engineering, product, ranking and research teams to take new techniques from experiment to production, across many languages and markets. Mentor scientists and engineers, raise the bar on experimental rigor, and communicate results and trade-offs clearly to technical and non-technical audiences. Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 4+ 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 3+ years related experience (e.g., statistics, predictive analytics, research) OR Doctorate in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 1+ year(s) related experience (e.g., statistics, predictive analytics, research) These requirements include but are not limited to the following specialized security screenings: Master's degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or a related field AND 6+ years related experience OR Doctorate in one of these fields AND 3+ years related experience OR equivalent experience. Solid foundation in information retrieval and search, including query understanding, retrieval, ranking and relevance evaluation. Experience building retrieval-augmented generation (RAG), agentic search or other systems that ground language model outputs in external data. Hands-on experience training or fine-tuning language models, such as supervised fine-tuning or reinforcement learning, and shipping the results to production. Experience designing evaluation metrics, test sets or model-based judges for search or answer quality. Solid experimentation skills, including statistical significance testing, A/B experiments and turning results into product decisions. Experience working with sensitive data under privacy constraints, including synthetic data generation. Proficiency in Python and modern machine learning frameworks such as PyTorch, and experience working with large-scale data. A track record of publications, patents or equivalent applied impact, and the ability to explain complex results to technical and non-technical audiences.