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Applied Scientist Intern, PhD (Summer 2027)

Name: RiseMe
Availability: InStock

San Francisco, CAHybridInternshipJunior / Entry-levelPosted 1 month agoVisa sponsorship available

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Required skills

Machine Learning
Artificial Intelligence
Data Science
Python
production coding environment
large language models
agent-based systems
empirical model evaluation
prototypes
open-ended technical problems

Nice to have

ML inference
simulation pipelines
evaluation pipelines
LLM agents
generative user simulation
agent-based modeling

Role summary

Lyft's Rider Science team is seeking an Applied Scientist intern to develop advanced user simulation methods using AI. The intern will create and validate LLM-based Rider Agents as behavioral proxies for real riders, studying their effectiveness in predicting responses to product changes before online testing. This role involves LLM engineering, agent-based modeling, machine learning, and causal inference, applied to real rider products. The intern will build a prototype simulation system, establish an evaluation framework for fidelity, and validate it with historical data. Responsibilities include developing agents, simulation environments, evaluation pipelines, analyzing emergent behaviors, conducting experiments, and communicating findings to partners.

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.

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