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Machine Learning​/Operations Research Platform Engineer

Job in Calgary, Alberta, D3J, Canada
Listing for: Kinaxis
Full Time position
Listed on 2026-09-27
Job specializations:
  • Software Development
    Software Engineer, Machine Learning/ ML Engineer, AI Engineer (Applied/Software)
Job Description & How to Apply Below
About Kinaxis
Are you looking to join an innovative, market‑leading company where you can truly elevate your career? At Kinaxis we are serious about culture, technology, customers, and not taking ourselves too seriously. If you want to be part of an incredible growth story, then we might just be the place for you! In 1984, we started out as a team of three engineers.

Today we have grown to a global organization with over 2,000 employees around the world, six global offices and a best‑in‑class headquarters in Ottawa, Canada. As winners of several Top Employer awards globally, we are proud to work with our customers and employees to solve some of the biggest challenges facing supply chains today. Kinaxis is a global leader in modern supply chain orchestration, powering complex global supply chains and supporting the people who manage them.

Our AI‑infused platform provides full transparency and visibility across end‑to‑end supply chains, enabling our customers to make faster, better decisions. We are trusted by renowned global brands to provide the agility and predictability needed to navigate today’s volatility and disruption. With more than 40,000 users in over 100 countries, we are expanding our team as we continue to innovate and revolutionize how we support our customers.

Location
Ottawa or Toronto, Canada – Hybrid. All other Canadian locations – Remote.

About The Team
ML/OR Platform Engineers at Kinaxis build the infrastructure and software that allows machine‑learning models and optimization algorithms to be trained, deployed, scaled, monitored, and used in our Supply Chain applications while also contributing directly to the algorithms themselves. Each one of us plays an important part in accomplishing our work, building our culture, and making a global impact. Every day, we are empowered to work together to help our customers make fast, confident planning decisions.

This is how we create a better planet – for each other, our customers and for generations to come.

What You Will Do

Investigate novel techniques combining class‑leading heuristics with optimization and ML

Translate real‑world Supply Chain Management use cases into mathematical models

Lead the design and implementation of mathematical models and ML systems

Define test strategies and develop comprehensive test plans

Write unit testing, integration testing, and debugging to ensure robust and error‑free software

Design, develop, and maintain automated test scripts for functional, regression, and performance testing using testing frameworks and tools

Collaborate closely with your agile team members and other stakeholders

Technologies We Use

C++/Python for core development

Commercial mathematical solvers

GPU Computing

Maestro’s market‑leading in‑memory data server technology

Visual Studio, JIRA, Confluence, Git

Sophisticated internal testing tools to validate correctness and performance

Virtual and cloud infrastructure for development, support and testing

What We Are Looking For

MSc or PhD in Computer Science, Machine Learning, Operations Research, Engineering, or related field

3+ years of software development experience, track record of delivering commercial software

Working knowledge of C++, including object‑oriented design, design patterns, and unit testing

Experience building and maintaining distributed services and frameworks in C++ and Python

Experience deploying and operating ML or optimization workloads in cloud or containerized environments

A love of data structures and algorithms, and the desire to apply them in the real world

Working knowledge of mathematical optimization and mixed‑integer programming concepts

Familiarity with commercial optimization solvers (Gurobi, Xpress, CPLEX) and their application in production systems

Ability to…
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