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.

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.

What you will do

• 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.

Basic Qualifications

• 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

Preferred Qualifications

• Knowledge in modern machine learning techniques applicable to privacy and recommender systems

• Advanced understanding of statistics, causal inference, and machine learning

• 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.

For New York, NY-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year. For San Francisco, CA-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year. For Sunnyvale, CA-based roles: The base salary range for this role is USD$174,000 per year - USD$193,500 per year. For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award & other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.

Uber is proud to be an Equal Opportunity/Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form- https://docs.google.com/forms/d/e/1FAIpQLSdb_Y9Bv8-lWDMbpidF2GKXsxzNh11wUUVS7fM1znOfEJsVeA/viewform