PhD Machine Learning Software Engineer Intern (Summer 2027)

Role

Lyft is seeking a PhD Machine Learning Engineer Intern for Summer 2027 on its Applied AI team. The intern will tackle an open research problem tied to product experiences used by millions of riders. Working with a Staff ML Engineer mentor, they will scope the problem, develop and evaluate methods on real data, and work toward a paper submission to a top ML venue.

Responsibilities

• Own a research project from problem framing and literature review through proposing methods and designing rigorous offline and online evaluations.
• Design, build, train, and test ML models in areas such as reinforcement learning, sequential decision-making, and 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 ML Engineers to frame research questions in a business context.
• Analyze experimental and observational data and communicate findings to technical and non-technical audiences.
• Write results for publication at a peer-reviewed venue with mentor and team support.
• Participate in code and specification reviews to ensure quality and share knowledge.

Qualifications

• Currently pursuing a PhD in Computer Science, Machine Learning, Statistics, Operations Research, Applied Mathematics, or a related technical field; returning to the program after the internship; and graduating between December 2027 and Summer 2028. These enrollment and timing requirements are mandatory.
• A track record of ML research demonstrated through publications, preprints, or substantial research projects.
• Strong foundation in reinforcement learning and sequential decision-making, particularly delayed or long-horizon rewards.
• Solid grounding in causal inference and counterfactual evaluation.
• Good understanding of ML libraries such as PyTorch, TensorFlow, or JAX.
• Strong programming skills in Python or a similar language.
• Proven ability to translate ML research papers into working code.
• Curiosity and ability to learn new concepts and technologies quickly.
• Strong problem-solving skills, resourcefulness, and ability to work independently through research or collaboratively through brainstorming.
• Demonstrated oral and written communication skills.

Preferred Qualifications

• Publications at venues such as NeurIPS, ICML, ICLR, KDD, WWW, RecSys, or AAAI.
• Experience with offline reinforcement learning, off-policy evaluation, learning from logged interaction data, recommender systems, or personalization.
• Practical knowledge of efficient end-to-end ML workflows on large-scale data, for example using Spark or SQL.
• Familiarity with online experimentation and A/B testing.

Location and Schedule

• San Francisco, California.
• Hybrid schedule with required in-office work on Mondays, Wednesdays, and Thursdays. Working in the office at least three days per week is an essential function of the role.

Compensation and Benefits

• Expected San Francisco base pay: $65–$68 per hour, depending on qualifications, experience, and geographic location.
• Medical, dental, and vision insurance options and mental health benefits.
• Holidays, two days of paid time off, and three days of sick time off for interns.
• 401(k) plan and subsidized commuter benefits.
• Lyft Pink opportunity to test new Ridership Program benefits.