About the role#
As a PhD Machine Learning Engineer Intern on our Applied AI team, you will take on an open research problem tied to product experiences used by millions of riders. You will work closely with a Staff ML Engineer mentor to scope the problem, develop and evaluate new methods on real data, and produce work suitable for a paper submission to a top ML venue. This role is for a PhD student who enjoys turning open-ended research questions into working systems and wants their research tested against real users and data.
What you'll do#
- Own a research project from start to finish by framing the problem, reviewing related work, proposing new methods, and designing rigorous evaluations.
- Design, build, train, and test ML models in areas such as reinforcement learning, sequential decision-making, and personalization.
- Write production-quality code to turn research prototypes into working pipelines on our 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 to both technical and non-technical audiences.
- Write up results for publication at a peer-reviewed venue with support from your mentor.
- Participate in code and spec reviews to ensure quality and distribute knowledge.
What you'll need#
- Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Operations Research, Applied Mathematics, or a related field.
- Must be returning to your program after the internship, with a graduation date between December 2027 and Summer 2028.
- A track record of ML research, evidenced by publications, preprints, or substantial research projects.
- A strong foundation in reinforcement learning and sequential decision-making, especially regarding delayed or long-horizon rewards.
- Solid grounding in causal inference and counterfactual evaluation.
- Proficiency in ML libraries like PyTorch, TensorFlow, or JAX, and strong programming skills in Python.
- Proven ability to turn research ML papers into working code.
- Strong problem-solving skills and the ability to learn new concepts quickly.
- Preferred: Publications at venues such as NeurIPS, ICML, ICLR, KDD, WWW, RecSys, or AAAI.
- Preferred: Experience with offline reinforcement learning, off-policy evaluation, recommender systems, or personalization.
- Preferred: Knowledge of end-to-end ML workflows on large-scale data and familiarity with A/B testing.
Location & details#
- This is a full-time, paid internship for Summer 2027.
- The role is based in San Francisco, California.
- This is a hybrid position requiring in-office attendance three days per week, specifically Mondays, Wednesdays, and Thursdays.
About Lyft
Lyft operates in the ground passenger transportation industry. Founded in 2012, the company provides rideshare, bike, and scooter services through a single mobile application. It is based in San Francisco and employs between 5,001 and 10,000 people. The organization functions as a public company.
How to get in at Lyft
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