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Software Engineer Intern, Machine Learning

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Software Engineer Intern, Machine Learning in Canada new

  • Toronto

Responsibilities

  • Design, build, train, and test machine learning models.
  • Write production-level code that converts ML models into working pipelines.
  • Partner with product managers, data scientists, and ML engineers to define business-relevant ML problems.
  • Analyze experimental and observational data and communicate findings to support decisions.
  • Participate in code and specification reviews to maintain quality and share knowledge.

Requirements

  • Currently pursuing a bachelor’s, master’s, or PhD in computer science or a related technical field at a university in Canada.
  • Expected graduation between December 2027 and Summer 2028.
  • Master’s candidates with prior work between bachelor’s and master’s programs must have less than two years of relevant full-time experience.
  • Available for the Summer 2027 internship in Toronto.
  • Knowledge of ML libraries such as scikit-learn, TensorFlow, PyTorch, Keras, or MXNet.
  • Strong programming skills in Python or a similar object-oriented language.
  • Ability to turn research ML papers into working code.
  • Practical knowledge of efficient end-to-end ML workflows.
  • Ability to design software systems and produce high-quality code.
  • Strong problem-solving, learning, research, collaboration, and communication skills.

Benefits

  • Mental health benefits.
  • Interns receive two paid days off and three sick days off in addition to holidays.
  • Subsidized commuter benefits and Lyft ride credits.
  • Summer 2027 internship in Toronto on a hybrid schedule requiring at least three days per week in the office, including Mondays, Wednesdays, and Thursdays.

Lyft

Lyft is a transportation network company that connects people to reliable rides, reshaping the way we navigate our communities. By leveraging innovative technology and AI, Lyft enhances mobility solutions tailored for urban lifestyles, making it easier for users to travel conveniently and efficie...

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