Skills for Machine Learning Resume
Machine learning roles demand a specific combination of maths fundamentals, Python/framework depth, and engineering skills to take models to production. This guide covers the skill clusters, keywords, and bullet examples that help ML resumes get noticed.
Core ML
- Supervised learning
- Unsupervised learning
- Deep learning
- NLP
- Computer vision
- Reinforcement learning
- Transformer architectures
- LLMs
Frameworks & libraries
- PyTorch
- TensorFlow
- scikit-learn
- HuggingFace Transformers
- Keras
- XGBoost
- LangChain
Data & pipeline
- SQL
- Pandas
- NumPy
- Apache Spark
- Airflow
- dbt
- Feature engineering
MLOps & deployment
- MLflow
- Kubeflow
- SageMaker
- Docker
- Kubernetes
- CI/CD for ML
- Model monitoring
- A/B testing
Resume bullet examples
- Trained and deployed a BERT-based document classifier achieving 94% accuracy; model now processes 2M+ documents/day in production on SageMaker.
- Reduced model retraining pipeline from 6 hours to 45 minutes by migrating to distributed training on AWS, saving $18K/month in compute.
- Built a recommendation system for 5M users using collaborative filtering; A/B test showed 12% lift in session engagement.
ML resumes live at the intersection of research depth and engineering credibility. Show both: models you designed and deployed, with scale (users served, requests/second) and business impact (revenue, engagement, cost). Open-source contributions and Kaggle rankings are worth including. WadeCV can tailor your ML skills to each role's focus — NLP, computer vision, MLOps, or LLM engineering.
Common mistakes to avoid
- Only showing Jupyter notebook projects — no production deployments
- Listing frameworks without showing what you built or the scale of impact
- No mention of model monitoring, drift detection, or retraining pipelines
Frequently asked questions
Should I list both PyTorch and TensorFlow on my ML resume?
Yes, if you have genuine experience with both. PyTorch dominates research and most modern ML teams; TensorFlow/Keras is more common in enterprise settings. List the one you're strongest in first and note your proficiency level if they differ significantly.