Applied Scientist II

Applying LLMs to improve search relevance and automate training data generation. Building state-of-the-art large-scale neural ranking models and feature processing frameworks. Leveraging reinforcement learning and user feedback signals to optimize long-term user engagement and retention. Developing scalable ML systems that power millions of search experiences every day. 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 Bachelor's Degree in Statistics, Econometrics, Computer Science, Electrical or Computer Engineering, or related field AND 5+ 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) OR equivalent experience. Solid background in machine learning, deep learning, and large-scale AI systems. Experience with LLMs, transformer-based models, retrieval, ranking, or recommendation systems. Solid understanding of neural network architectures, feature engineering, and model optimization. Experience developing and deploying production-scale ML systems. Proficiency in Python and familiarity with modern ML frameworks such as PyTorch or TensorFlow. Solid software engineering skills, including distributed systems, data processing, and system design. Experience with reinforcement learning, online experimentation (A/B testing), or user engagement optimization is a plus. Excellent problem-solving, communication, and collaboration skills.