ML Platform Engineer Intern – Hybrid, Toronto

Lyft; Toronto; Híbrido; CAD 63.000 - 73.000; A tiempo parcial

Ventajas ofrecidas por este puesto de trabajo

Mental health benefits

2 days paid time off

3 days sick time off

Subsidized commuter benefits and the公司

Descripción de la vacante

United States Digital Space LLC is seeking a student intern in Toronto to contribute to an ML platform in a hybrid in-office environment. You will design, train, and test ML models and write production-level code to turn models into working pipelines.

The role requires studies in CS or a related field at a Canadian university, with graduation dates between late 2027 and mid-2028, and strong Python skills. Expect collaborative work with cross-functional teams and opportunities to translate

Formación

• Pursuing a bachelor’s, master’s, or PhD in Computer Science or a related technical field from a university in Canada (required).
• Graduation date between Dec 2027 and Summer 2028 (required).
• For master's candidates with a gap between degrees: less than 2 years of relevant full-time work experience.

Responsabilidades

• 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 ML Engineers to frame ML 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 knowledge sharing.

Conocimientos

Python programming

ML libraries

Research to code translation

Good communication

Team collaboration

Educación

Bachelor’s/Master’s/PhD in CS or related

Herramientas

scikit-learn

TensorFlow

PyTorch

Keras

MXNet

Descripción del empleo

United States Digital Space LLC is seeking a student intern in Toronto to contribute to an ML platform in a hybrid in-office environment. You will design, train, and test ML models and write production-level code to turn models into working pipelines.

The role requires studies in CS or a related field at a Canadian university, with graduation dates between late 2027 and mid-2028, and strong Python skills. Expect collaborative work with cross-functional teams and opportunities to translate