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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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We serve pharmaceutical R&D teams racing to cut years off vaccine development timelines. Our platform models antigen stability and adjuvant interactio...
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Our mission is to make AI systems provably fair before they reach production, not after a regulator or a lawsuit forces the question. Python, scikit-...
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We process risk scoring for card-not-present transactions on behalf of mid-market e-commerce platforms handling millions of orders a month. In this r...
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You'll be the newest member of a seven-person industrial AI team supporting manufacturing clients running around-the-clock production lines. We alrea...
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Our clients include hospital systems where a missed drug interaction during a care transition can directly harm a patient, so reliability here is non-...
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The models you ship in this role decide what a brand-new user sees in their very first ten minutes on a client's app, before any interest graph or ses...
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Our clients include institutional trading desks where reliability and auditability of every risk figure are non-negotiable. Our portfolio and credit s...
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Our clients include audit committees and general counsel offices at companies where an undisclosed conflict of interest can trigger a regulatory inves...
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Our mission is to make AI explainable to non-technical stakeholders. This role extends that mission beyond models we build ourselves, into models our...
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We just signed three new enterprise clients who each maintain hundreds of internal policy documents, and manually tracking what changed between versio...
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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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