Applied Scientist Intern, PhD (Summer 2027)

About the Role

Lyft's Rider Science team is seeking an Applied Scientist Intern for Summer 2027 to develop and validate LLM-based Rider Agents as behavioral proxies for real riders. The project will investigate when agent simulations provide reliable signals about rider responses to product interventions before online experimentation.

The role combines LLM engineering, agent-based modeling, machine learning, and causal inference. Expected outcomes include a working Rider Agent simulation prototype, an evaluation framework for simulation fidelity, and validation using historical rider experiments.

Responsibilities

• Develop LLM-based Rider Agents representing diverse rider contexts, preferences, histories, and behaviors.
• Build agent-based simulation environments to evaluate rider interactions with product experiences and interventions.
• Build evaluation pipelines assessing realism, robustness, and mechanism plausibility against human data or established theory.
• Analyze emergent behaviors and interaction dynamics across user segments and marketplace conditions.
• Conduct experiments and ablation studies on agent behavior, interaction dynamics, and simulation validity.
• Apply the framework to 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.

Qualifications

• Currently pursuing a PhD in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field, with graduation between December 2027 and Summer 2028; required.
• Proficiency in 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 and the ability to collaborate to solve problems.
• Familiarity with A/B testing, causal inference, or experimental design.

Preferred Qualifications

• Experience building production-level ML inference, simulation, or evaluation pipelines.
• 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.
• Publications in relevant venues such as NeurIPS, ICLR, AAAI, ICML, or ACL/EMNLP.
• Interest in simulation platforms supporting hypothesis generation, intervention testing, or human-AI system design.

Work Arrangement

Hybrid in San Francisco, with required office attendance on Mondays, Wednesdays, and Thursdays. Working in the office at least three days per week is an essential function of the role.

Compensation and Benefits

• Expected San Francisco base pay: $64–$68 per hour, depending on qualifications, experience, and geographic location.
• Medical, dental, and vision insurance options and mental health benefits.
• Holidays, two days of paid time off, and three days of sick time off for interns.
• 401(k) plan and subsidized commuter benefits.
• Lyft Pink opportunity to test new Ridership Program benefits.