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Machine learning engineers bridge the gap between data science research and production systems. They take ML models from prototype to production, building robust pipelines for training, evaluation, and serving. With the explosion of generative AI and large language models, ML engineers who can fine-tune, deploy, and optimize foundation models are in extraordinary demand.
Australia's tech industry is concentrated in Sydney, Melbourne, and Brisbane. The market offers strong salaries (especially in fintech, mining tech, and government contracts), excellent work-life balance, and 20 days annual leave minimum. The time zone can be a challenge for global collaboration but benefits Asia-Pacific focused companies.
Work authorization: Australia's Skilled Worker visa (subclass 482) covers most tech roles, with ICT occupations on the priority migration list. The Global Talent visa (subclass 858) offers permanent residency for highly skilled tech professionals. Processing times have improved significantly.
ML Engineer → Senior ML Engineer → Staff/Principal ML Engineer → ML Architect → Head of ML/AI → VP of AI. The field is evolving rapidly, with specializations emerging in LLM infrastructure, computer vision, NLP, and reinforcement learning.
Production ML experience is what separates candidates. Having models in production — not just notebooks — is the key differentiator. Understand the full ML lifecycle: data collection, feature engineering, training, evaluation, deployment, and monitoring. Familiarity with LLMs and generative AI is increasingly expected.
ML engineers train and evaluate models, build feature pipelines, deploy models to production, monitor model performance and data drift, optimize inference latency and cost, and collaborate with data scientists on experiment-to-production handoffs. Debugging model behavior in production is a frequent challenge.