Machine Learning/Operations Research Platform Engineer
Kinaxis Inc.2 months ago
Remote, CanadaMid Level
Responsibilities
- Investigate techniques combining heuristics, mathematical optimization, and machine learning.
- Translate real-world supply-chain management use cases into mathematical models.
- Lead the design and implementation of mathematical models and machine-learning systems.
- Build infrastructure and software for training, deploying, scaling, monitoring, and using ML models and optimization algorithms.
- Develop and maintain distributed services and frameworks in C++ and Python.
- Define test strategies, develop test plans, and perform unit testing, integration testing, and debugging.
- Create automated test scripts for functional, regression, and performance testing.
- Collaborate with agile team members and stakeholders.
- Apply AI and other tooling to accelerate the software development lifecycle while maintaining architectural consistency, secure design, and code quality.
Requirements
- MSc or PhD in Computer Science, Machine Learning, Operations Research, Engineering, or a related field.
- At least 3 years of software development experience and a record of delivering commercial software.
- Working knowledge of C++, 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.
- Knowledge of data structures, algorithms, mathematical optimization, and mixed-integer programming concepts.
- Familiarity with Gurobi, Xpress, CPLEX, and their use in production systems.
- Ability to design and maintain automated functional, regression, and performance testing.
- Familiarity with GPU-accelerated computing, distributed optimization systems, or high-performance computing is highly desirable.
- Nice-to-have experience includes supply-chain management, NVIDIA CUDA, cuOpt, PDLP, large-scale optimization systems, MLOps, model lifecycle management, training pipelines, and inference services.
Benefits
- Hybrid work in Ottawa or Toronto and remote work from other Canadian locations.
- Flexible vacation and Kinaxis Days, which are company-wide days off.
- Flexible work options.
- Physical and mental well-being programs and regularly scheduled virtual fitness classes.
- Mentorship, training, career development, recognition programs, referral rewards, and hackathons.
- Inclusive recruitment process with accommodations available upon request.
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