Applied Scientist Intern (Summer 2027)
The Applied Scientist Intern (Summer 2027) joins the Rider Science team to develop next-generation user simulation methods using state of the art AI. The role focuses on building and validating LLM-based Rider Agents that serve as behavioral proxies for real riders to provide reliable signals regarding product interventions before online experimentation. You will build agent-based simulation systems grounded in rider context, evaluate their fidelity against historical data, and develop evaluation pipelines to assess realism and robustness. This work involves solving problems related to pre-experiment evaluation and hypothesis generation. Required skills include proficiency in Python, experience with large language models or agent-based systems, and a strong foundation in machine learning and empirical model evaluation. The role also requires familiarity with causal inference, experimental design, and the ability to develop prototypes for complex technical problems within the rider product space.
What does a Applied Scientist earn?
Median $231000 from 40 postings across 11 companies.
Develop LLM-based Rider Agents that represent diverse rider contexts, preferences, and behaviors.
Build agent-based simulation environments to evaluate rider interactions with different product experiences.
Create evaluation pipelines to measure the realism and robustness of simulated behavior against human data.
Analyze emergent behaviors and interaction dynamics within simulated populations under various marketplace conditions.
Conduct experiments and ablation studies on agent behavior and simulation validity.
Apply the simulation framework to real Rider products for hypothesis generation and pre-experiment evaluation.
Communicate technical findings and recommendations to science, engineering, and product partners.
What we're looking for
Currently pursuing a PhD 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 experience 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 solve open-ended technical problems.
Strong verbal and written communication skills for collaborating with science, engineering, and product partners.
Familiarity with A/B testing, causal inference, or experimental design.
Experience with production ML pipelines, LLM agents, computational social science, or a publication record in relevant venues (preferred).