LLM Rider Agent Scientist Intern (Hybrid)

Lyft; San Francisco (CA); Hybrid; USD 88,000 - 94,000; Part time

Benefits offered by this job

Health benefits

Mental health benefits

Paid time off

401(k)

Commuter benefits

Lyft Pink program

Job summary

Lyft is seeking an Applied Scientist intern to develop next-generation Rider Agent simulations using state-of-the-art AI. You will build agent-based models grounded in real rider data to evaluate interventions and accelerate product iteration, with a focus on LLM engineering, causal inference, and empirical evaluation.

The role sits in the Rider Science team and emphasizes prototype development, rigorous evaluation, and collaboration across science, engineering, and product teams in a hybrid

Qualifications

• 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

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

Skills

Python

LLMs

Agent-based systems

Machine learning

Experimental design

Communication

Education

PhD in Computer Science / ML / AI / Data Science (expected 2027-2028)

Job description

Lyft is seeking an Applied Scientist intern to develop next-generation Rider Agent simulations using state-of-the-art AI. You will build agent-based models grounded in real rider data to evaluate interventions and accelerate product iteration, with a focus on LLM engineering, causal inference, and empirical evaluation.

The role sits in the Rider Science team and emphasizes prototype development, rigorous evaluation, and collaboration across science, engineering, and product teams in a hybrid