Backed by a16z and Founders Fund.
About the role
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. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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 can provide reliable signal about rider responses to product interventions before online experimentation.
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.
This role combines LLM engineering, agent-based modeling, machine learning, and causal inference with direct applications to real-world rider products. The expected outcome is to build a working Rider Agent simulation prototype, establish an evaluation framework for measuring simulation fidelity and validate the framework using...
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