Job description
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
Job requirements
About the ideal candidate:
Advanced degree (PhD or Master’s degree) in operations research, applied or computational mathematics, or computer science with a focus on optimization.
Hands-on experience developing or extending optimization algorithms (e.g., simplex, interior-point, branch-and-bound, constraint propagation, heuristics, local search, nonlinear solvers).
Proficiency in C++ with solid software engineering skills (data structures, memory management, debugging, profiling).
Familiarity with numerical linear algebra and its role in optimization solver performance.
Ability to design algorithms with attention to numerical stability, scalability, and computational efficiency.
Experience with existing solvers (e.g., Gurobi, CPLEX, SCIP, HiGHS, OR-Tools, IPOPT).
· Exposure to parallel/distributed computing (multicore, GPU, or cluster environments).
Knowledge of domain-specific combinatorial optimization problems (e.g., scheduling, routing, supply chain, energy systems).
Familiarity with AI/ML integration into optimization pipelines (heuristics generation, ML-guided branching, LLM-driven model formulation).
Contributions to open-source optimization or numerical computing libraries.
Experience building and evaluating Multi-Agent AI systems and with tools such as LangChain, Dify, MCP.
Track record of publications in top AI/OR venue.
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