Applied Scientist Intern, PhD (Summer 2027)

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

Requirements

• in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field, with a graduation date between
• Proficiency with Python and working in a production coding environment
• Hands-on experience with large language models or agent-based systems
• 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

Preferred

• 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

Experience

• Currently pursuing a
• PhD degree

December 2027 and Summer 2028 (required)

• Strong foundation in machine learning and empirical model evaluation
• Familiarity with A/B testing, causal inference, or experimental 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.