Applied Scientist Intern, PhD (Summer 2027)

• 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

• Great medical, dental, and vision insurance options • Mental health benefits • In addition to holidays, interns receive 2 days paid time off and 3 days sick time off • 401(k) plan to help save for your future • Subsidized commuter benefits • Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law. Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. The expected base pay range for this position in the San Francisco area is $64-$68/hour. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

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 historical rider experiments. 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 Experience: • 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 Benefits: • Great medical, dental, and vision insurance options • Mental health benefits • In addition to holidays, interns receive 2 days paid time off and 3 days sick time off • 401(k) plan to help save for your future • Subsidized commuter benefits • Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law. Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. The expected base pay range for this position in the San Francisco area is $64-$68/hour. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process. Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.