About the Job
At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.
The Lyft 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 ca...
You will build agent-based simulation systems grounded in real rider context and behavioral data, evaluate their fidelity against observed rider behavior and historical experiments, and study where these simulations can accelerate product iteration and 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
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)
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
