Python ML & Optimization Engineer

Qualifications and requirements

• Python: Development skills with experience implementing, modifying, and troubleshooting production-grade ML or mathematical optimization solutions.
• Optimization / Solvers: Hands-on optimization and solver experience.
• Machine Learning: Experience working with machine learning platforms.
• Experience tuning, retraining, or reconfiguring optimization/ML solutions for different operational constraints.
• Ability to work independently in an existing codebase where documentation and subject-matter knowledge may be limited.

Job functions

• Reverse-engineer and understand an existing optimization model/solver created by another team.
• Modify and customize the existing solution for different operational constraints.
• Adapt the Australian implementation to North American plant scheduling and paint/coating line requirements.
• Implement, modify, and troubleshoot production-grade ML or mathematical optimization solutions.
• Tune, retrain, or reconfigure optimization and ML solutions based on changing operational requirements.
• Support a manufacturing scheduling solution designed to minimize production transitions, machine downtime, and changeovers while maximizing throughput.
• Work independently within an existing codebase where documentation and subject-matter knowledge may be limited.

Benefits

• Contrato a largo plazo.
• 100% Remoto.
• Vacaciones y PTOs
• Posibilidad de recibir 2 bonos al año.
• 2 revisiones salariales al año.
• Clases de inglés.
• Equipamiento Apple.
• Plataforma de cursos en linea
• Budget para compra de libros.
• Budget para compra de materiales de trabajo
• mucho mas..

Nice to have skills

• Azure Machine Learning experience. Azure ML is desirable but should not be an absolute screening requirement if the candidate has strong ML/optimization fundamentals.
• Experience with manufacturing scheduling, production optimization, constraint optimization, or supply-chain scheduling.
• Experience working with sequencing problems involving manufacturing jobs, machine downtime, production changeovers, or similar operational constraints.

The role focuses on reverse-engineering and adapting an existing optimization solution for manufacturing scheduling.

The engineer will take an existing model/solver created by another team, understand it, and modify/customize it rather than developing an algorithm from scratch.

The application supports a manufacturing process where the sales team gathers customer orders, with customers providing the metal and paint, sometimes on short notice. The tool is designed to minimize transition time during production. A paint change can require approximately 20 minutes, so the solution helps reduce these transitions and maximize throughput by grouping paints by color, grouping steel dimensions, and minimizing downtime on the production line.