Machine Learning Engineer Co-op

CIBC•Toronto, ON

2d•Hybrid

About The Position

CIBC’s Enterprise AI Infrastructure & ML Ops team is hiring a Machine Learning Engineer Co-op. As an ML Engineer Co-op, you will collaborate on the MLOps strategy, assist with adherence to enterprise governance, and support in the implementation of tooling and frameworks to enable secure, scalable, and repeatable AI/ML delivery across the bank. Success will be built on supporting technical activities to standardize capabilities and create a measurable impact in building influence with stakeholders. At CIBC we enable the work environment most optimal for you to thrive in your role. Details on your work arrangement (proportion of on-site and remote work) will be discussed at the time of your interview. We may ask you to complete an attribute-based assessment and other skills tests (such as simulation, coding, French proficiency, MS Office). Our goal for the application process is to get to know more about you, all that you have to offer, and give you the opportunity to learn more about us. You must be currently enrolled in post-secondary education and returning to full-time studies to be eligible. However, you do not need to be in a registered Co-Op program to be considered for a role. This is an 8 month co-op opportunity.

Requirements

Currently enrolled in post-secondary education and returning to full-time studies.
Strong programming skills.
Knowledge of data science & machine learning with Python using common data science libraries, such as the PyData stack, and other ML frameworks.
Azure and cloud related knowledge.
Familiarity in using Git, GitHub or other version control tools is an asset.
Strong sense of intellectual curiosity.
Values matter to you.
Bring your real self to work and live CIBC values – trust, teamwork and accountability.

Nice To Haves

Knowledge of writing efficient modular codes in a non-notebook environment.
Hands-on experience building end-to-end solutions with Large Language Models.

Responsibilities

Collaborate on the MLOps strategy.
Assist with adherence to enterprise governance.
Support in the implementation of tooling and frameworks to enable secure, scalable, and repeatable AI/ML delivery across the bank.
Support technical activities to standardize capabilities and create a measurable impact in building influence with stakeholders.
Use knowledge in Machine Learning to contribute towards the development of reproducible models.
Utilize Python programming skills to refactor experimental notebooks into production grade Python modules using functional programming.
Assist with tasks related to the deployment of machine learning models and the maintenance of internal Python libraries.
Adhere to the team’s code-style, work-flow patterns and project structure standards.
Participate in writing great documentation, practicing test-driven-development and properly annotating your code.