machine learning engineer

A software engineer who designs, trains, evaluates, deploys and maintains machine learningmachine learning, n.: A way of building software that learns from examples instead of following rules written by a programmer. Shown thousands of labeled photos, for instance, a program can learn to recognize cats. models in production systems, bridging data science and software engineering. Typical responsibilities include building data and featurefeature engineering, n.: Turning raw data into the inputs a model learns from, using knowledge of the problem; for example, converting a timestamp into 'hour of day' and 'is it a weekend'. pipelines, selecting and training models, tuning hyperparametershyperparameter, n.: A setting chosen by people before training, such as how fast a model learns or how big it is, as opposed to the internal values the model learns by itself from data., optimizing models for latencylatency, n.: How long you wait between asking an AI system for something and getting the answer. Lower latency means a snappier, more responsive system. and cost, packaging and deployingmodel deployment, n.: Putting a trained AI model to work in a real product so that people or other software can use it — and doing so carefully enough that the change can be undone if something goes wrong. them, and setting up monitoringmodel monitoring, n.: Continuously checking an AI model after it goes live — what data it receives, what it predicts and whether it is still right — so that problems are caught before they cause harm. and retraining under MLOpsMLOps, n.: The working habits and tools that keep machine-learning models dependable inside real products once the experiment is over: automated testing, release, monitoring and retraining; the factory and maintenance crew around a model, not the model itself. practices. Compared with a data scientist, an ML engineer concentrates less on analysis for business decisions and more on production code and scale; compared with a research scientist, less on new methods and more on reliable application. At AI laboratories the title also covers engineers who build large-scale training and inference infrastructure. Since 2023 the newer title AI engineerAI engineer, n.: A software developer who builds products using ready-made AI models — connecting them to data and tools and testing that they work well — rather than inventing or training the models. has been used for engineers who build on pretrained models instead of training their own.

The machine learning engineer rewrote the feature pipeline so that training and serving share the same code.