Applied Scientist Intern (Summer 2027)
San Francisco · USD 64-68 / hour · Hybrid · Posted Sep 28, 2026
At a glance
- Workplace
- Hybrid
- Location
- San Francisco
- Salary
- USD 64-68 / hour
About the role
- The Lyft Rider Science team is seeking an Applied Scientist intern to develop next-generation user simulation methods using state-of-the-art AI.
- The role focuses on developing and validating LLM-based Rider Agents to serve as behavioral proxies for real riders.
- The project aims to study how agent simulations can provide reliable signals about rider responses to product interventions.
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
- Analyze emergent behaviors and interaction dynamics in simulated populations
- Conduct experiments and ablation studies on agent behavior, interaction dynamics, and simulation validity
- Apply the simulation framework to real Rider product problems
- Communicate technical findings and recommendations to science, engineering, and product partners
Requirements
- Currently pursuing a PhD degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related technical field
- Graduation date between December 2027 and Summer 2028
- 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
- Familiarity with A/B testing, causal inference, or experimental design
Preferred qualifications
- 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
- Medical, dental, and vision insurance options
- Mental health benefits
- 2 days paid time off
- 3 days sick time off
- 401(k) plan
- Subsidized commuter benefits
- Lyft Pink membership
Workplace
- In-office on a hybrid schedule
- Expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays
Skills
- Python
- LLM
- Machine Learning
- Agent-based Modeling
- Causal Inference
- A/B Testing
- Experimental Design
