Co-op/Intern Developer, Machine Learning
Role Overview
The machine learning development team builds supply chain planning solutions for Maestro users.
This placement provides hands-on experience with machine learning software development and production deployment components.
The position is open to co-op students and interns.
Key Skills for This Role
Full Job Posting
About Kinaxis
Kinaxis develops AI-infused supply chain orchestration software used by customers worldwide.
The company builds solutions that help organizations manage complex global supply chains.
Team and Opportunity
The machine learning development team builds supply chain planning solutions for Maestro users.
This placement provides hands-on experience with machine learning software development and production deployment components.
The position is open to co-op students and interns.
Location and Work Arrangement
- In Ottawa or Toronto, the role requires at least three days per week in the office.
- For other Canadian locations, the role is remote.
Term Duration
- This is a full-time 4- or 8-month placement starting January 2027.
- Extensions can typically be made after the internship begins.
Compensation
- Compensation ranges from CAD 27.04 to CAD 39.66 per hour.
- The final offer reflects the candidate's skills, year of education, and experience.
What You Will Do
- Work on machine learning development projects from technology selection through production code.
- Write Python code for microservice architectures.
- Contribute to AI platform scalability, security, and continuous improvement.
- Participate in agile ceremonies, pair programming, and cross-functional delivery.
- Develop product ideas and support demos and patent applications.
What We Are Looking For
- Be enrolled in full-time education or be a recent or upcoming graduate within 12 months of the placement end date.
- Have completed at least 2 years of a computer science or engineering program.
- Have hands-on machine learning experience from work terms, hackathons, competitions, or similar activities.
- Optimization and statistical inference experience are assets.
Technologies
- Technologies include Python, Docker, Kubernetes, Azure, and GitHub Actions.
Benefits
- Benefits may include flexible vacation, well-being programs, mentorship, training, career development, recognition programs, and hackathons.
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