Researcher - AI-Powered Optimization Solver

Huawei Canada has an immediate permanent opening for a Researcher.

About the team:

The Intelligent Cloud Infrastructure Lab aims to innovate technologies, algorithms, systems, and platforms for next-generation cloud infrastructure. The lab addresses scalability, performance, and resource utilization challenges in existing cloud services while preparing for future challenges with appropriate technologies and architectures. Additionally, the lab aims to understand industry dynamics and technology trends to create a robust ecosystem.

About the job:

• Design and implement core algorithms for linear programming (LP), mixed-integer programming (MIP), nonlinear programming (NLP), constraint programming (CP), and combinatorial optimization problems such as vehicle routing problems (VRP).

• Develop and design solver infrastructure that is robust, scalable, and extensible, with attention to numerical stability, performance, and memory efficiency.

• Optimize performance across multiple fronts (cutting-plane generation, branching strategies, presolve techniques, heuristics, decomposition methods, parallelization).

• Collaborate with AI/LLM researchers to integrate optimization solvers into agentic AI workflows (e.g., automated model formulation, scenario analysis).

• Advance solver-AI synergy by exploring methods for algorithm selection, parameter tuning, and automatic solver configuration with machine learning or LLMs.

The base salary for this position ranges from $100,000 to $150,000 depending on education, experience and demonstrated expertise

About the ideal candidate:

• Advanced degree (PhD or Master’s) in Operations Research, Applied/Computational Mathematics, or Computer Science with a focus on optimization.

• Proven hands-on experience developing or extending optimization algorithms (e.g., simplex, interior-point, branch-and-bound, heuristics, nonlinear solvers).

• Strong proficiency in C++ with solid software engineering foundations (data structures, memory management, debugging, and profiling).

• Familiarity with numerical linear algebra, algorithmic scalability, and computational efficiency principles.

• Experience using or integrating existing optimization solvers (e.g., Gurobi, CPLEX, SCIP, HiGHS, OR-Tools, IPOPT).

• Exposure to parallel or distributed computing environments (multicore, GPU, or clusters).

• Experience with AI/ML and multi-agent systems integration into optimization pipelines (e.g., LangChain, Dify, MCP).

• Record of contributions to open-source optimization tools or publications in leading AI/OR venues.