PhD Machine Learning Software Engineer Intern (Summer 2027)

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

Experience:

Benefits:

• Great medical, dental, and vision insurance options
• Mental health benefits
• In addition to holidays, interns receive 2 days paid time off and 3 days sick time off
• 401(k) plan to help save for your future
• Subsidized commuter benefits
• Lyft Pink - Lyft team members get an exclusive opportunity to test new benefits of our Ridership Program
• Lyft is an equal opportunity employer committed to an inclusive workplace that fosters belonging. All qualified applicants will receive consideration for employment without regards to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, age, genetic information, or any other basis prohibited by law. We also consider qualified applicants with criminal histories consistent with applicable federal, state and local law.
• Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office 3 days per week on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers.
• The expected base pay range for this position in the San Francisco area is $65-$68/hour. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.
• Total compensation is dependent on a variety of factors, including qualifications, experience, and geographic location. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.

Requirements

• 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