Kinaxis is hiring a

Co-Op/Intern Machine Learning Developer, ML Solutions

Toronto, Canada

At Kinaxis, who we are is grounded in our common belief that people matter. Each one of us plays an important part in accomplishing our work, building our culture and making a global impact.

Every day, we’re empowered to work together to help our customers make fast, confident planning decisions. This is how we create a better planet – for each other, for our customers and for generations to come. Our cloud-based platform RapidResponse ensures that the products we need – everything from medicine and cars, to day-to-day items like toothpaste – make it to market and into our hands when we need them with minimal ecological footprint.

We make the world better, and you can too.

Co-Op/Intern Machine Learning Developer, ML Solutions

Job location: our office in Toronto, Canada

Term length: 8 or 12 months starting January 2024

About the Team

We closely work with the data science and ML-as-a-Service Platform teams to design, implement and scale high performing big data AI solutions to demand forecasting and supply chain problems in retail.

We tackle all aspects of the ML lifecycle, build tools to automate it and libraries to scale it. We are highly skilled engineers who are not afraid to tackle hard ML problems in forecasting and optimization and author full ML solutions to ship scalable big data products.

What you will do

  • Help develop scalable ML solutions out of the approved client prototypes via our ML libraries
  • Participate in scrum teams developing ML products for demand sensing in different industry verticals
  • May perform other duties as assigned

What we are looking for

  • Excellent software engineering skills
  • Good understanding of machine learning algorithms, data structures, pipelines and transformations
  • Proficiency in Python and object-oriented programming
  • Capable of processing data with pandas and PySpark (e.g. querying, transforming, joining, cleaning, etc.), with some experience in numpy and sklearn
  • Ability to work in Linux environments and public clouds with containerization technologies like Docker


Things that Would Definitely Help

  • Machine learning Ops in the cloud with containerization (e.g. Kubernetes, Argo, Workflows)
  • Familiarity with retail and CPG business domain
  • Experience building data science products


What we have to offer

  • Challenging Work - We love solving highly complex problems. And as the global leaders in our industry, we never stop innovating—our work is never “done. That’s because across our teams and in all roles, every employee is empowered to bring their best ideas forward and to jump in and solve the problems they’re passionate about.
  • Great People - We take our work seriously, but we don’t take ourselves too seriously! It’s in our DNA to celebrate, laugh, and have fun. We are stronger, together, when we are open, honest, and above all, real. Every person is valued here and plays an important role in our shared success.
  • Global Impact - As a global team spanning continents, boundaries, and cultures, every day we are inspired by the impact our work has on our colleagues, our customers, our communities, and the world at large.
  • Diversity, Equity and Inclusion - Diversity, equity and inclusion are more than words to us. They are the guiding principles for building a culture where we celebrate each others’ differences, continuously strive for equality and recognize that inclusion makes us stronger as individuals, a company and a global citizen. 

For more information, visit the Kinaxis web site at or the company’s blog at

Kinaxis strongly encourages diverse candidates to apply to our welcoming community. We strive to make our website and application process accessible to any and all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please contact us at [email protected]. This contact information is for accessibility requests only and cannot be used to inquire about the status of applications.

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