About the Job
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 im...
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...
Key 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
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
