Uber Senior Applied Scientist Privacy Engineering 

United States, West Virginia 

25605873

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.

* Accommodations may be available based on religious and/or medical conditions, or as required by applicable law. To request an accommodation, please reach out to .