Applied Scientist Intern, PhD (Summer 2027)

About the Role

The Rider Science team is seeking an Applied Scientist intern to develop next-generation user simulation methods using state-of-the-art AI methods. The goal of this project is to develop and validate LLM-based Rider Agents that can serve as behavioral proxies for real riders, and study when agent simulations can provide reliable signal about rider responses to product interventions before online experimentation.

Key Responsibilities

• Develop LLM-based Rider Agents that represent heterogeneous rider contexts, preferences, histories, and behaviors.
• Build agent-based simulation environments for evaluating rider interactions with different product experiences and interventions.
• Build evaluation pipelines to assess realism, robustness, and mechanism plausibility of simulated behavior against human data or established theory.
• Analyze emergent behaviors and interaction dynamics in simulated populations under different user segment and marketplace conditions.
• Conduct experiments and ablation studies on agent behavior, interaction dynamics, and simulation validity.
• Apply the simulation framework to real Rider product problems and assess its usefulness for hypothesis generation, product iteration, and pre-experiment evaluation.
• Communicate technical findings and recommendations to science, engineering, and product partners.

Requirements & Tech Stack

• 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.
• Familiarity with A/B testing, causal inference, or experimental design.

Bonus Points

• 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.

Qualifications

• 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 (required).