These are the ATS keywords, credentials, and software terms that recur across current machine learning engineer job descriptions — organized so you can scan for what your resume is missing. Placement matters as much as presence: an ATS keyword buried in a skills list ranks lower than the same term used in context inside an experience bullet.
Title variants
- Machine Learning Engineer
- ML Engineer
- Applied Scientist
- MLOps Engineer
- AI Engineer
- Research Engineer
Core languages
- Python
- Scala
- Java
- Go (for serving infrastructure)
ML frameworks
- PyTorch
- TensorFlow
- scikit-learn
- XGBoost
- LightGBM
- Hugging Face Transformers
- JAX
MLOps and serving
- MLflow
- Weights & Biases
- BentoML
- TorchServe
- ONNX
- Seldon
- Triton Inference Server
Feature and data
- PySpark
- Spark
- Feast
- Tecton
- feature store
- dbt
- Airflow
Infrastructure
- AWS SageMaker
- GCP Vertex AI
- Azure ML
- Kubernetes
- Docker
- GPU clusters
Methods
- model deployment
- model monitoring
- A/B testing for models
- feature engineering
- model fine-tuning
- RAG
- LLM
- vector embeddings
Long-tail phrases
- machine learning engineer resume examples
- how to write an ML engineer resume
- MLOps engineer resume
- AI engineer cv template
- machine learning skills for resume
Pasting these terms into your resume verbatim without matching context can read as keyword stuffing to a human reviewer, even if it passes the ATS. The full Machine Learning Engineer resume guide shows where each category belongs and how to work it into real experience bullets.