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Applied Scientist Intern (Summer 2027)

Join Lyft as an Applied Scientist Intern to develop AI-driven user simulation methods for rider behavior analysis.

Location
San Francisco, CA
Compensation
$64–$68/hr
Level
intern
Type
internship · Hybrid

Posted by employer 1 day ago

First seen on Joblaze 1 day ago

Last verified on the company career page 1 day ago

What you'll build

  • Develop LLM-based Rider Agents
  • Build agent-based simulation environments
  • Build evaluation pipelines for simulated behavior
  • Analyze emergent behaviors in simulated populations
  • Conduct experiments on agent behavior and simulation validity

Must have

  • Currently pursuing a PhD degree in a related field
  • Proficiency with Python
  • Hands-on experience with large language models
  • Strong foundation in machine learning
  • Ability to independently develop prototypes
  • Strong verbal and written communication skills

Nice to have

  • Experience building production level ML inference pipelines
  • Prior research experience with LLM agents
  • Background in computational social science
  • Experience evaluating AI systems against human data
  • Familiarity with prompting in LLM-based agents
  • Publication record in relevant venues

Practical constraints

  • In-office 3 days per week on Mondays, Wednesdays, and Thursdays

AI in the day-to-day

Develop and validate LLM-based Rider Agents for simulating rider behavior.

Requirements

Education
PhD

Not disclosed in this posting: years of experience, visa sponsorship.

Benefits

401k Match Commuter Benefits Paid Time Off Sick Leave Health Insurance Mental Health Benefits

Joblaze summary

The Applied Scientist Intern at Lyft focuses on developing advanced user simulation methods, specifically creating LLM-based Rider Agents that mimic real rider behaviors. This role requires proficiency in Python and experience with machine learning, particularly in agent-based modeling and causal inference. It is well-suited for PhD candidates in relevant fields who can tackle complex technical challenges independently. The position is part of a collaborative team environment, emphasizing hands-on experimentation and product iteration.

Joblaze insights

  • Listed yesterday — first seen on Joblaze September 29, 2026. Last confirmed on Lyft's careers page September 29, 2026.
  • Machine Learning appears in 44.4% of 72 comparable intern ai/ml roles in United States; Python appears in 30.6% of 72 comparable intern ai/ml roles in United States.

Quick facts

Is the Applied Scientist Intern (Summer 2027) role remote?
It's hybrid — Lyft expects some on-site time in San Francisco, CA.
What's the salary range?
Lyft lists $64–$68 for this role.
Where is the role based?
Lyft is hiring for this position in San Francisco, CA.
What's the tech stack?
Joblaze extracted these technologies from the posting: Artificial Intelligence, Data Science, Machine Learning, Python.
What seniority level is this role?
Lyft targets intern candidates for this position.
Is this full-time or contract?
Internship for this Applied Scientist Intern (Summer 2027) role at Lyft.

From the original posting

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 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.

Standard company text repeated across Lyft's postings is omitted here.

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