Machine Learning/Operations Research Platform Engineer

Summary

Kinaxis is a global leader in supply chain orchestration, providing an AI-infused platform for managing complex global supply chains. The Machine Learning/Operations Research Platform Engineer will build infrastructure and software for training, deploying, scaling, monitoring, and using machine-learning models and optimization algorithms, while contributing to mathematical models and algorithms. The role also involves testing, debugging, and collaborating with agile teams and stakeholders.

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

Skills

• 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
• 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

Qualifications

Must Haves

• 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
• 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

Nice to Haves

• Familiarity with GPU-accelerated computing frameworks, distributed optimization systems, or high-performance computing environments is highly desirable

Benefits

• Flexible vacation and Kinaxis Days (company-wide days off)
• Flexible work options
• Physical and mental well-being programs
• Regularly scheduled virtual fitness classes
• Mentorship programs, training, and career development
• Recognition programs and referral rewards
• Hackathons