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!

• →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

• Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Operations Research, Applied Mathematics, or a related technical field, and returning to your program after the internship, with a graduation date between December 2027 and Summer 2028 (required)
• A track record of ML research, shown through publications, preprints, or substantial research projects
• Strong foundation in reinforcement learning and sequential decision making, especially problems with delayed or long-horizon rewards
• Solid grounding in both causal inference and counterfactual evaluation
• Good understanding of ML libraries like PyTorch, TensorFlow, or JAX
• Strong programming skills in Python or a similar language
• Proven ability to effectively turn research ML papers into working code
• Curiosity and ability to quickly learn new concepts and technologies
• Strong problem solving mindset, resourcefulness, and willingness to figure things out independently through research or collaboratively through brainstorming
• Demonstrated oral and written communication skills
• Bonus Points
  
  • Publications at venues such as NeurIPS, ICML, ICLR, KDD, WWW, RecSys, or AAAI
  • Experience with offline reinforcement learning, off-policy evaluation, or learning from logged interaction data, recommender systems or personalization
  • Practical knowledge of how to build efficient end-to-end ML workflows on large-scale data (for example Spark or SQL)
  • Familiarity with online experimentation and A/B testing