Machine Learning/Operations Research Platform Engineer

Kinaxis Inc.•Remote,

3d•Hybrid

About The Position

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, for our customers and for generations to come. Our cloud-based platform Maestro 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. Our customers have terabytes of data that needs to be analyzed for the largest supply chains imaginable.

Requirements

MSc or PhD in Computer Science, Machine Learning, Operations Research, Engineering, or related field
3+ year of software development experience, track record of delivering commercial software
Working knowledge of C++, including object-oriented design and design patterns, 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 design, develop, and maintain automated test scripts for functional, regression, and performance testing using testing frameworks and tools
Ability to find opportunities to accelerate the SDLC through innovative application of AI or other tooling, while upholding architecture consistency, secure design, and code-quality standards
Ability to review AI-generated code rigorously for correctness, architectural fit, integration risk, and edge case support with a growth mindset and bias for experimentation

Nice To Haves

Knowledge of Supply Chain Management (Demand Planning, MRP, S&OP, Capacity Planning)
Experience with GPU computing, NVIDIA CUDA, cuOpt, PDLP, or large-scale optimization systems
Experience with MLOps, model lifecycle management, training pipelines, and inference services
Familiarity with GPU-accelerated computing frameworks, distributed optimization systems, or high-performance computing environments is highly desirable

Responsibilities

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