ML Engineer Intern — Build & Deploy Models (Toronto)

Socket.dev; Toronto; Hybrid; CAD 63,000 - 73,000; Part time

Benefits offered by this job

Mental health benefits

2 days paid time off + 3 sick days

Subsidized commuter benefits

Job summary

Lyft is offering a machine learning internship in Toronto for students pursuing a CS degree in Canada. You will design, train, and test ML models and translate research into production-ready code.

Collaborate with PMs, data scientists, and fellow ML engineers to frame problems within business context. You will turn research papers into working code and build end-to-end ML workflows, delivering high-quality software in a dynamic, collaborative environment.

Qualifications

• Currently pursuing a Bachelor's, Master's, or PhD degree in Computer Science or a related technical field from a university in Canada (required).
• Available Summer 2027 for the internship in Toronto (required).
• Strong programming skills in Python or a similar object oriented language.
• Experience turning research ML papers into working code and building end-to-end ML workflows.
• Engineer at heart with ability to design software systems and produce quality code.

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 ML problems.
• Analyze experimental and observational data and communicate findings.
• Participate in code and spec reviews to ensure code quality.

Skills

Python

Research & experimentation

Problem solving

Collaborative work

Communication

Education

Bachelor's/Master's/PhD in CS (Canada)

Tools

TensorFlow

PyTorch

Keras

MXNet

scikit-learn

Job description

Lyft is offering a machine learning internship in Toronto for students pursuing a CS degree in Canada. You will design, train, and test ML models and translate research into production-ready code.

Collaborate with PMs, data scientists, and fellow ML engineers to frame problems within business context. You will turn research papers into working code and build end-to-end ML workflows, delivering high-quality software in a dynamic, collaborative environment.