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Training, evaluating, and deploying models that learn from data.
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Machine Learning engineering sits at the intersection of data science and software engineering, taking statistical models from research and making them work reliably in production. ML Engineers build the training pipelines, evaluation frameworks, and serving infrastructure that turn model experiments into real systems. Research Scientists push the state of the art, developing novel architectures and techniques. The field spans classical ML, deep learning, and the increasingly specialized world of foundation model research.
Highlighted pills — primary tools most commonly listed in job descriptions for this discipline.
Don't conflate ML Engineer with Data Scientist or AI Engineer, these are three distinct profiles. ML Engineers are production-focused software engineers who specialize in model systems. Data Scientists analyze and experiment. AI Engineers build applications with pre-trained models. When a JD asks for all three skill sets, that's worth a conversation with the hiring manager before sourcing.