Resume Keywords for a Machine Learning Engineer (ATS Skills List)
ATS parsers for ML roles match on frameworks (PyTorch, TensorFlow), techniques (NLP, computer vision, deep learning), and deployment tooling (MLflow, Kubernetes, feature stores), so use the exact terms from the posting. Distinguish research skills from production skills, because many postings screen specifically for deployment experience. Spell out abbreviations like NLP and CV once so both the acronym and full term match.
Top ATS Keywords for a Machine Learning Engineer Resume
Most employers store applications in an applicant tracking system (Workday, Greenhouse, Taleo, iCIMS). The system parses your file into plain text, and a recruiter then searches that text for terms from the posting. These are the terms recruiters search for this role — work in the ones you can honestly claim.
Core Hard Skills
Machine LearningDeep LearningNatural Language ProcessingModel DeploymentFeature EngineeringMLOpsComputer VisionStatistical Modeling
Tools, Systems & Software
PythonPyTorchTensorFlowscikit-learnMLflowAmazon SageMaker
Certifications & Credentials
AWS Certified Machine Learning – SpecialtyTensorFlow Developer CertificateGoogle Cloud Professional Machine Learning EngineerAzure AI Engineer Associate
Soft Skills ATS Scans For
Problem SolvingCommunicationCollaborationAnalytical ThinkingExperimentationCross-functional Teamwork
Why These Keywords Matter for Machine Learning Engineers
| Keyword | Why recruiters & ATS weight it |
|---|---|
| PyTorch | The leading deep-learning framework in production and research; postings list it as a core requirement. |
| MLOps | Signals you can operate models in production, the skill that separates ML engineers from data scientists in filtering. |
| Model Deployment | Names the production focus hiring managers screen for over pure model-building. |
| TensorFlow | Widely required alongside or instead of PyTorch, so listing it broadens ATS matches. |
| Natural Language Processing | A major specialization that recruiters filter for explicitly, especially with the LLM boom. |
| Feature Engineering | A distinct, frequently required skill that demonstrates data maturity beyond model training. |
| LLM | Large language model experience is increasingly demanded; the term now appears in most new ML postings. |
| SageMaker | A common production platform whose exact name recruiters search when the stack is AWS-based. |
How to Place Keywords So the ATS Reads Them
- Mirror the exact wording from the job posting (both the acronym and the spelled-out term, e.g. “CRM (Salesforce)”).
- Put your strongest keywords in your summary and your two most recent roles — ATS weights recent experience.
- Add a dedicated Skills section, but also weave keywords into your bullet points so they read naturally.
- Use standard section headings (“Work Experience”, “Skills”) and avoid tables, text boxes, or headers/footers that ATS parsers drop.
- Never keyword-stuff or use white text — modern parsers and recruiters both catch it.
Put These Keywords Into Strong Bullets
Keywords get you past the filter; quantified bullets win the interview. See Machine Learning Engineer resume bullet examples to see these terms in action.
Frequently Asked Questions
Which frameworks should I list as keywords?
List the framework named in the posting first, whether PyTorch or TensorFlow, then include others you have genuine experience with. Add supporting tools like scikit-learn, MLflow, and your deployment platform so the ATS matches the full modern ML stack.
Do I need certifications to pass ATS for ML roles?
Certifications are not required, but the AWS Machine Learning Specialty, TensorFlow Developer Certificate, and cloud ML engineer credentials are searched and can help. List only certifications you hold, and let quantified project outcomes carry most of the weight.
How do I handle NLP, CV, and LLM keywords?
Spell out natural language processing, computer vision, and large language model once each, then use the acronym so parsers match both forms. Only include the specializations you have real work in, and back each with a bullet so the keyword is substantiated.
Resume Keywords for Related Roles
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