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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.
The US tech market is the world's largest, with Silicon Valley, Seattle, New York, Austin, and other hubs offering the highest salaries globally. Remote work has distributed opportunities more broadly, though major tech companies are increasingly requiring office presence. The market is competitive but rewards specialized skills handsomely.
Work authorization: Most tech professionals enter on H-1B visas (annual lottery, employer-sponsored) or L-1 visas (intra-company transfers). The O-1 visa serves individuals with extraordinary ability. Green card processing through employer sponsorship can take several years depending on country of birth.
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.