Job no: P9QT9

• Build AI and machine learning solutions from the ground up.
• Work with cutting-edge technologies.
• Join a global mining technology business.

Job Summary

Join a global mining technology leader as a Machine Learning Engineer focused on evaluating, benchmarking and improving the performance of LLM and agent-based AI systems.

You'll develop evaluation frameworks, measure model effectiveness and help ensure our AI solutions deliver reliable, scalable outcomes for customers across the mining industry while contributing to the ongoing evolution of our AI capabilities.

Key Selling Points

• Build AI and machine learning solutions from the ground up.
• Work with cutting-edge technologies.
• Join a global mining technology business.

About Us

We are a global mining software business bringing together the expertise of Micromine, Motion Metrics and Fast2Mine.

Our solutions help mining companies explore, design, plan and operate more effectively through industry-leading software, AI, machine vision and automation technologies. From geology and mine planning to fleet performance and operational optimisation, we help customers improve productivity, efficiency and sustainability across the mining value chain.

Backed by Weir's global reach and resources, we combine deep mining expertise with a strong innovation roadmap, delivering trusted technology solutions used by mining operations around the world.

The Opportunity

As a Machine Learning Engineer, you'll help evaluate, benchmark and improve the performance of LLM and agent-based AI systems across our product suite. You'll develop evaluation frameworks, measure model performance and provide insights that help deliver reliable, high-impact AI solutions for the mining industry.

The Impact you’ll have

• Design, develop and deploy machine learning models using PyTorch and related technologies.
• Design and implement evaluation frameworks for LLM and agentic AI systems.
• Develop benchmarks, metrics and test datasets to assess model quality and reliability.
• Analyse model performance and identify opportunities for improvement.
• Partner with product and engineering teams to define AI success measures.
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