Machine Learning/Operations Research Platform Engineer
RemoteCalifornia, United States or Ottawa, Ontario, Canada
Job Summary
Investigate novel techniques combining leading heuristics with optimization and machine learning, then translate real-world supply chain use cases into mathematical models. Lead the design and implementation of these systems while defining test strategies and developing comprehensive plans. Write unit, integration, and debugging code to ensure robust software, alongside designing automated test scripts for functional, regression, and performance testing using established frameworks. Collaborate closely with agile team members and stakeholders to deploy and operate ML or optimization workloads in cloud or containerized environments.
Required Qualifications
- 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
Desired Qualifications
- Familiarity with GPU-accelerated computing frameworks, distributed optimization systems, or high-performance computing environments is highly desirable
- 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
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