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Browse vetted remote Scikit-learn positions at AI companies — ML engineering, classical model development, and feature engineering roles.
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Have you ever wanted your work to directly protect academic integrity for students who never see the systems working behind the scenes? Our mission is...
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Our mission is to keep supply chains trustworthy for the manufacturers and retailers who depend on accurate freight documentation to run their operati...
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You'll be the third detection engineer on a team of 11, working directly with our SOC analysts who triage alerts around the clock. Our stack is Python...
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Our threat detection models degrade faster than we can retrain them, and attackers adapt within weeks. We're looking for a research scientist to build...
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We're building tools that help clinical teams spend less time on paperwork and more time with patients, and we need an applied scientist to push our m...
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We just expanded our risk modeling team after a strong year and need to move fast without cutting corners on model safety. Our clients include institu...
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Have you ever wondered which songs actually keep listeners on a platform versus which ones just get skipped after ten seconds? We don't care where you...
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Our mission is to make property risk visible before closing day, not after. This role focuses on flagging title defects — liens, easements, boundary d...
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You'll be the third ML engineer on a team of twelve, working directly with the plant reliability engineers who currently rely on scheduled inspections...
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Our clients include Fortune 500 companies in financial services, and one of them just flagged a spike in peer-to-peer payment fraud that their current...
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Role guide
Scikit-learn is the workhorse of applied machine learning. Remote Scikit-learn jobs involve building production ML models for classification, regression, and clustering tasks — often as part of a larger data platform or AI feature pipeline. Many AI companies use Scikit-learn alongside PyTorch or XGBoost in production systems.
Are there remote jobs specifically for Scikit-learn engineers?
Scikit-learn is rarely the only requirement — it typically appears alongside Python, Pandas, and sometimes PyTorch. Roles involving applied ML, prediction systems, or feature engineering are the best fit.
Is Scikit-learn relevant if a company uses PyTorch or TensorFlow?
Yes. Scikit-learn handles preprocessing, classical baselines, and evaluation utilities that complement deep learning frameworks. Most ML engineers use Scikit-learn daily regardless of their primary modelling framework.
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