Applied Scientist Intern, Phd (Summer 2027)

AI Summary

Job Description

Develop next-generation user simulation methods using AI. Build LLM-based Rider Agents as behavioral proxies for real riders, study their responses to product interventions before online experimentation, and evaluate simulation fidelity against real rider data and historical experiments.

Hard Requirements

• Currently pursuing a PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field, with a graduation date between December 2027 and Summer 2028
• Proficiency with Python and working in a production coding environment
• Hands-on experience with large language models or agent-based systems
• Strong foundation in machine learning and empirical model evaluation
• Ability to independently develop prototypes and work through open-ended technical problems
• Strong verbal and written communication skills, and ability to collaborate and communicate with others to solve a problem

Soft Requirements

• Familiarity with A/B testing, causal inference, or experimental design
• Experience building production level ML inference, simulation, or evaluation pipelines
• Prior research experience with LLM agents, generative user simulation, or agent-based modeling
• Background in computational social science or behavioral modeling
• Experience evaluating AI systems against human behavioral data, qualitative studies, or controlled experiments
• Familiarity with prompting, tool use, memory, planning, or coordination in LLM-based agents
• Publication record in relevant venues such as NeurIPS, ICLR, AAAI, ICML or ACL/EMNLP
• Interest in building simulation platforms that support hypothesis generation, intervention testing, or human-AI system design