Software Engineer Intern, Machine Learning (Summer 2027 - Toronto)

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 peta-byte 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.

If you are a student with experience in machine learning workflows, passionate about solving challenging problems using data and working in a dynamic, creative, and collaborative environment, this opportunity is for you!

Responsibilities:

• Contribute to the design, build, train and test of Machine Learning models
• Write production-level code to convert ML models into working pipelines
• Partner with Product Managers, Data Scientists, and fellow ML Engineers to frame Machine Learning problems within the business context
• Analyze experimental and observational data, communicate findings to support decisions
• Participate in code and spec reviews to ensure code quality and distribute knowledge

Experience:

• Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required), with a graduation date between December 2027 and Summer 2028 (required). For any candidates who are master's students who worked between their bachelor's and master's programs: candidates should also have less than 2 years of relevant full-time work experience
• Available during Summer 2027 for the internship in Toronto
• Good understanding and knowledge of ML libraries like scikit-learn, Tensorflow, PyTorch, Keras, MXNet, etc.
• Strong programming skills in Python or a similar object oriented language
• Proven ability to effectively turn research ML papers into working code
• Practical knowledge of how to build efficient end-to-end ML workflows
• “Engineer at heart” with a high degree of comfort in designing software systems and producing high-quality 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 brainstor