PhD Machine Learning Software Engineer Intern (Summer 2027)

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. With over half a billion rides and counting, Lyft is solving hard problems in a flourishing domain with a lot of data and creative solutions in Marketplace, Mapping, Fraud, Growth and beyond. We're actively building the next-generation Machine Learning (ML) platform for low-cost, ultra-immersive transportation to improve people’s lives using modern ML with petabyte-scale data. Our Machine Learning Engineers are excited to work on these challenging problems and redefine solutions to directly impact various aspects of Lyft's primary business. As a PhD Machine Learning Engineer Intern on our Applied AI team, you'll take on an open research problem tied to product experiences used by millions of riders. Working closely with a Staff ML Engineer mentor, you'll scope the problem, develop and evaluate new methods on real data, and take the work far enough that it can be shared with the research community, with the goal of a paper submission to a top ML venue. If you are a PhD student who enjoys turning open-ended research questions into working systems, and you want your research to be tested against real users and real data, this opportunity is for you!

Responsibilities:

Own a research project from start to finish: frame the problem, review related work, propose new methods, and design rigorous offline and online evaluations Design, build, train and test ML models in areas such as reinforcement learning, sequential decision-making, personalization Write production-quality code that turns research prototypes into working pipelines on Lyft's data and ML infrastructure Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame research questions within the business context Analyze experimental and observational data, and communicate findings clearly to both technical and non-technical audiences Write up results for publication at a peer-reviewed venue, with support from your mentor and the team Participate in code and spec reviews to ensure code quality and distribute knowledge