Software Engineer Optimisation
Employment Info
JOB SUMMARY
Requirements
- Strong experience in C++ development (performance-oriented or systems-level preferred)
- Experience working with mathematical optimisation (linear programming/mixed integer programming solvers)
- Familiarity with AMPL or equivalent modelling languages
- Understanding of mixed integer programming, graph theory, and optimisation theory
- Experience building high-performance desktop applications
- Experience integrating external libraries, APIs and solver engines
- Strong problem-solving skills with ability to work on complex computational problems
Responsibilities
- Develop and maintain core optimisation logic using C++ and AMPL
- Integrate and optimise solver engines (Gurobi, AMPL Solver, Maroma)
- Build and enhance desktop UI workflows for scenario modelling and analysis
- Implement APIs and integrations with external tools and data sources
- Support batch execution, automation, and performance tuning
- Contribute to source control, builds, and regression testing
- Work on large-scale optimisation problems involving millions of decision variables and constraints
- Contribute to performance improvements and solver efficiency
Desired Qualifications
- Experience with mining optimisation, supply chain optimisation, or scheduling problems
- Experience integrating or working with advanced solver libraries such as Maroma or similar optimisation engines
- Exposure to large-scale optimisation systems handling multi-year planning horizons and large datasets
- Experience with performance tuning and scaling optimisation models
- Familiarity with cloud-enabled or distributed execution patterns, including remote execution of heavy compute workloads
- Experience building or consuming APIs for optimisation services (e.g., remote execution, automation pipelines)
- Knowledge of desktop UI frameworks (e.g., DevExpress) and workflow-driven UX design
- Exposure to data integration with external tools
- Experience with automation / batch processing frameworks for scenario analysis and “what-if” simulations
- Understanding of data governance, security, and handling sensitive operational datasets
- Familiarity with modernising legacy products into scalable architectures.
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