ML Engineer: LLM & AI Evaluation (Hybrid)

Micromine; Perth; Hybrid; AUD 120,000 - 180,000; Full time

Benefits offered by this job

Hybrid work model

Competitive remuneration

Career development

14 weeks paid parental leave

Health and wellbeing support

Job summary

Micromine in Australia is seeking a Machine Learning Engineer to evaluate, benchmark and improve LLM and agent-based AI systems across the product suite. You’ll design evaluation frameworks, measure model performance and provide insights that drive reliable, high-impact AI solutions for the mining industry.

You'll work with PyTorch, develop benchmarks, and collaborate with product and engineering teams to define AI success metrics in a hybrid work environment with strong career development

Qualifications

• Proven experience developing ML/AI solutions.
• Experience in ML evaluation and benchmarking for LLM or agentic systems.
• Advanced Python skills and familiarity with ML libraries (NumPy, Pandas, Scikit-learn).
• Solid understanding of neural networks, computer vision, NLP, or related AI disciplines.

Responsibilities

• Design, develop and deploy ML models using PyTorch and related technologies.
• Design 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.

Skills

Python

PyTorch

NumPy

Pandas

Scikit-learn

NLP

Education

Bachelor's degree in Computer Science or Data Science/AI

Tools

PyTorch

NumPy

Pandas

Scikit-learn

Job description

Micromine in Australia is seeking a Machine Learning Engineer to evaluate, benchmark and improve LLM and agent-based AI systems across the product suite. You’ll design evaluation frameworks, measure model performance and provide insights that drive reliable, high-impact AI solutions for the mining industry.

You'll work with PyTorch, develop benchmarks, and collaborate with product and engineering teams to define AI success metrics in a hybrid work environment with strong career development