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Machine Learning Engineer Jobs

Machine learning engineers turn research and data into working, production systems - owning training pipelines, model iteration, and deployment across vision, language, and speech. It is the most common engineering path into AI and spans nearly every industry now shipping ML-powered products.

655 live Machine Learning Engineer roles across the AI employers we track - updated hourly, apply directly.

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Machine Learning Engineer roles are rare: 655 live right now. Get the new ones every Monday.

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Latest Machine Learning Engineer roles

The most portable title in AI

Machine learning engineer is the broadest and most portable title in this field. It appears at frontier labs, at software companies with no AI product of their own, and at banks, retailers, logistics operators, and healthcare systems, and the underlying work stays recognizable across all of them: get data into a usable state, train or fine-tune a model, evaluate it honestly, ship it, and monitor it. That portability is the main career argument for the title.

Most production machine learning is still classical supervised learning rather than generative modeling. Ranking, recommendation, forecasting, fraud and risk scoring, pricing, routing, demand prediction, and churn models run continuously inside businesses that will never train a language model, and they are built with gradient-boosted trees and modest neural networks over tabular and behavioral data. Candidates who prepare only for generative AI interviews are preparing for the smaller share of the openings.

The line against AI engineer is training ownership. If a role expects you to own a training or fine-tuning loop, curate datasets, and answer for offline metrics, it is machine learning engineering. If it expects you to build a product on top of a model someone else trained and reached through an API, it is AI engineering. Titles are used inconsistently between employers, so read the responsibilities: the presence or absence of training work is the reliable signal.

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Frequently asked questions

How many live Machine Learning Engineer openings are there?
We are tracking 655 live Machine Learning Engineer roles across the AI companies we monitor, updated hourly. Each listing links straight to the employer's own application page.
What is the difference between a machine learning engineer and a data scientist?
Machine learning engineers own models in production, covering pipelines, serving, latency, monitoring, and retraining, and are judged on whether the system works reliably. Data scientists more often own analysis, experimentation, and measurement, and hand off recommendations. The boundary varies by company: at smaller employers one person does both.
Do machine learning engineer roles require deep learning experience?
Many do not. A large share of production machine learning is supervised learning over tabular and behavioral data, where gradient-boosted trees remain a strong default. Deep learning experience is essential for vision, speech, and language work, and is increasingly expected at AI-first employers, but it is not a universal requirement for the title.
Which industries hire machine learning engineers outside AI companies?
Financial services for risk, fraud, and pricing; retail and marketplaces for search, ranking, and recommendation; logistics for forecasting and routing; healthcare and insurance for claims and clinical prediction; and manufacturing, energy, and telecommunications. These employers usually post through general enterprise applicant tracking systems rather than AI-specific channels.

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