Co-op ML Engineer: Build Real-World AI Solutions

ODAIA; Toronto; On-site; CAD 38,000 - 56,000; Full time

Job summary

RBC Borealis in Toronto, Canada, invites a Co-op Machine Learning Software Engineer to join a team tackling ML problems from data pre-processing to deployment. You’ll work on end-to-end projects with access to massive datasets and modern compute resources, collaborating with researchers on reinforcement learning, computer vision, and more.

The role focuses on building ML-based software, refining algorithms, and turning prototypes into polished solutions, while documenting usage and decisions.

Qualifications

• Pursuing a Bachelor’s or Master’s degree in Computer Science, Computer Engineering, Software Engineering, or equivalent.
• Experience with software development (including co-ops/internships) is expected.
• Proficiency in one of the major languages (C++, C#, Java, Python).
• Familiarity with Unix/Linux command line and scripting is preferred.

Responsibilities

• Build ML-based software solutions.
• Collaborate with research and business teams to converge on best solutions.
• Optimize algorithms and prototypes for efficient implementation.
• Extend prototypes into fully functional solutions ready for internal/external use.
• Document usage, design decisions, and capabilities.
• Handle large datasets using distributed frameworks like Hadoop and Spark.

Skills

Active Learning

Adaptability

Communication

Creativity

Interpersonal Relationships

Listening Effectively

Personal Development

Personal Initiative

Teamwork

Education

Bachelor's or Master's in CS/CE/SE

Tools

Hadoop

Spark

TensorFlow

PyTorch

Keras

Theano

Unix

Bash

SQL

NoSQL

Job description

RBC Borealis in Toronto, Canada, invites a Co-op Machine Learning Software Engineer to join a team tackling ML problems from data pre-processing to deployment. You’ll work on end-to-end projects with access to massive datasets and modern compute resources, collaborating with researchers on reinforcement learning, computer vision, and more.

The role focuses on building ML-based software, refining algorithms, and turning prototypes into polished solutions, while documenting usage and decisions.