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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.
London remains Europe's largest tech hub, with Manchester, Edinburgh, and Bristol growing rapidly. Post-Brexit, the UK operates its own immigration system with a Skilled Worker visa route. Salaries in London are among the highest in Europe, though the high cost of living offsets some of the advantage. Financial services and healthtech drive significant demand.
Work authorization: The UK Skilled Worker visa requires employer sponsorship. Tech roles typically qualify under the shortage occupation list, which reduces visa fees and salary thresholds. The Global Talent visa offers an alternative for those with exceptional talent or promise in tech.
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.
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