Python ML & Optimization Engineer

Improving; United States; Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Long-term contract

100% Remote

Vacation and PTOs

Two annual bonuses

Two salary reviews per year

English classes

Apple equipment

Online courses platform

Budget for books

Budget for work materials

Job summary

Ulvior is seeking an experienced ML/Optimization engineer to design, implement, and optimize production-grade ML and optimization solutions. You will work with Python, solvers, and scheduling challenges across manufacturing lines, including NA adaptations.

This long-term, 100% remote role offers ongoing learning opportunities, salary reviews, English classes, and access to training budgets.

Responsibilities

• Develop and maintain production-grade ML/optimization solutions in Python.
• Tune and troubleshoot models to meet production needs.
• Reverse-engineer and understand an existing optimization model/solver.
• Modify and customize the existing solution for different operational constraints.
• Adapt NA plant scheduling and line requirements from an Australian implementation.
• Implement, modify and troubleshoot production-grade ML or optimization solutions.
• Tune, retrain, or reconfigure optimization and ML solutions as requirements change.
• Support a manufacturing scheduling solution to minimize transitions, downtime and changeovers while maximizing throughput.
• Work independently within an existing codebase with limited documentation.

Skills

Python

Optimization

Machine Learning

Model Tuning

Independent Work

Reverse Engineering

Customization

Adaptation

Scheduling

Sequencing

Tools

Azure ML

Job description

• 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.

• 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.

• 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.

• 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..