About the Team

Our mission is to make Uber the industry model for consumer privacy through differentiated, highly scalable, and extensible products and services, engineering standards, policy, and open communications. We are focusing on building both a privacy technology platform and user-facing products that give our users more control over their data, build trust, advance data privacy, and enable our business.

Lead efforts within the organization to drive the development and AI/ML-based solutions in support of user-facing products, downstream services, or infrastructure tools and platforms used across Uber.

About the Role

Lead efforts to develop and evaluate large-scale traditional machine learning models, optimize retrieval-augmented generation (RAG) systems, fine-tune large language models (LLMs), and implement agentic workflows. This role requires a strong foundation in both traditional machine learning and advanced LLM technologies.

• Develop and evaluate large-scale machine learning models systems in production.
• Propose, design, and analyze large scale online experiments
• Define and implement metrics to measure product performance
• Present findings to business and executive audiences
• Collaborate with engineers and product managers to implement ideas and plan future roadmaps
• Optimize retrieval-augmented generation (RAG) systems for enhanced performance and relevance.
• Fine-tune large language models (LLMs) to improve predictive accuracy and operational efficiency.
• Implement agentic workflows to streamline processes and enhance decision-making.

• Ph.D., MS or Bachelors degree in Statistics, Economics, Operations Research, Computer Science, Engineering, or other quantitative field
• If Ph.D or M.S. degree, a minimum of 2+ years of industry experience as an Applied Scientist or equivalent
• Knowledge of underlying mathematical foundations of machine learning, statistics, optimization, economics, and analytics
• Hands-on experience building and deployment ML models.
• Knowledge of experimental design and analysis
• Experience with exploratory data analysis, statistical analysis and testing, and model development
• Ability to use a language like Python or R to work efficiently at scale with large data sets
• Proficiency in technologies in one or more of the following: SQL, Spark, Hadoop

• Knowledge in modern machine learning techniques applicable to privacy and recommender systems
• Advanced understanding of statistics, causal inference, and machine learning
• 5+ years of industry experience as an Applied Scientist or equivalent.
• Experience designing and analyzing large scale online experiments
• Experience working with large scale data sets using technologies like Hive, Presto, and Spark
• Experience with synthetic data generation.
• Proficiency in fine-tuning and optimizing large language models (LLMs).
• Experience in retrieval-augmented generation (RAG) systems.
• Familiarity with agentic workflows and their applications in machine learning and AI systems.