ML engineering postings screen for the ability to ship models to production, not just train them. Name the frameworks, the serving/MLOps stack, and the measurable model impact.
Match these keywords automatically →Machine learning · Deep learning · Model deployment (MLOps) · Feature engineering · Distributed training · Model evaluation · Data pipelines · A/B testing
Python · PyTorch · TensorFlow · scikit-learn · Kubernetes · Docker · AWS SageMaker · MLflow · Spark · Airflow
Problem solving · Cross-functional collaboration · Communication · Ownership
AWS Certified Machine Learning – Specialty · TensorFlow Developer Certificate
✓ Deployed a real-time recommendation model (PyTorch + SageMaker) serving 2M users, lifting click-through 18%.
List both methods and frameworks ("deep learning", "PyTorch") and the serving stack ("MLOps", "Kubernetes") — postings search for the production skills, not just modeling. Don’t paste the whole list — choose the keywords you genuinely match, weave them into accomplishments with real numbers, and mirror the exact phrasing of the specific posting. Align Resume reads the job description and aligns your resume to it automatically, so the right keywords appear truthfully and you get a match score before you apply.
What are the top resume keywords for a Machine Learning Engineer?
High-value keywords include Machine learning, Deep learning, Model deployment (MLOps), Feature engineering, Python, PyTorch, TensorFlow. Use the ones you genuinely have, matching the exact wording in the job posting.
How do I get my Machine Learning Engineer resume past the ATS?
Use a clean single-column layout, mirror the posting’s exact skill and tool names where you qualify, and quantify your results. List both methods and frameworks ("deep learning", "PyTorch") and the serving stack ("MLOps", "Kubernetes") — postings search for the production skills, not just modeling.