Data & AI · Skill guide

How to put Machine Learning on your resume

Machine learning as a resume skill is screened for the full arc — framing, features, training, deployment, monitoring — with library names (scikit-learn, PyTorch, XGBoost) and shipped-model evidence carrying the ranking weight.

It appears in 29% of 2,367 engineering postings in our 15 August 2026 snapshot . Densest in Machine Learning Engineer (82%), Data Scientist (47%), and Data Engineer (29%) .

Check my keyword coverage free Against any job description

Keywords that ride along with Machine Learning

Job descriptions rarely ask for Machine Learning alone — these co-terms appear in the same postings and are screened together. Cover the ones you've genuinely used.

Public ATS boards (Arbeitnow, Greenhouse, Ashby, Google Careers, Lever, RemoteOK, Remotive). Not LinkedIn or Indeed. Percentages are share of postings that already mention Machine Learning.

Example bullets that prove the skill

A skill in a list is a claim; a skill in a bullet with an outcome is evidence. Patterns to adapt with your real numbers:

“Trained and deployed an XGBoost fraud model (precision 0.91 at target recall) scoring 100% of transactions in real time”
“Built the feature pipeline and drift monitoring for 5 production models, catching two silent degradations before business impact”

Machine Learning resume FAQs

How do I list Machine Learning on my resume?

Put "Machine Learning" in your skills section in its exact form, then prove it in at least one experience bullet with context and an outcome — a skill that appears only in a list reads as padding to recruiters. Machine learning as a resume skill is screened for the full arc — framing, features, training, deployment, monitoring — with library names (scikit-learn, PyTorch, XGBoost) and shipped-model evidence carrying the ranking weight.

Which keywords should appear alongside Machine Learning?

Job descriptions that ask for Machine Learning typically also screen for: Python, LLM, SQL, AWS, Kubernetes. Include the ones you've genuinely used — co-keyword coverage is how ATS ranking separates real practitioners from list-padders.

Should I list Machine Learning if I've only used it a little?

List it only if you could answer interview questions about it comfortably. A safer honest framing for light experience is grouping it under a "familiar with" tier, separate from your core skills — inflated skill claims surface fast in technical interviews.

Does your resume actually show Machine Learning?

Paste a job description that requires it — RoleTuner shows whether your resume surfaces Machine Learning and its co-keywords the way an ATS reads them, free.