Machine Learning Resume Examples and Writing Tips for 2026

Machine learning resume template (text format)

Use the template below to help format your resume and make your qualifications and skills clear to employers.

How to write a machine learning resume

In the fast-growing field of machine learning, your resume is your key to getting noticed.

As a Machine Learning Engineer, you need to show employers that you grasp key concepts and have practical experience. A machine learning job typically entails designing, developing, and implementing advanced machine learning models and algorithms to solve real-world problems by utilizing large datasets. This includes building predictive algorithms, neural networks, and recommendation systems.

Even without direct company experience, emphasizing your relevant skills and projects, including those from self-learning and supplementary experience, can help you stand out and secure your dream job.

1. Showcase your ML and AI projects

If you’ve been honing your skills through machine learning projects outside of work, you should list the projects on your resume.

Adding projects to your resume is a good idea whether you’re applying to your first machine learning engineer position, transferring from another field outside of data science, or you’ve recently graduated with a degree in computer science. That’s because it shows employers you have ML skills, even without formal experience or a MS/PhD degree in ML.

When describing your machine learning projects, demonstrating your understanding of ML algorithms and techniques is key. Include links to your GitHub repository for each project, and aim to include a mix of traditional machine learning and deep learning.

Here’s how to list machine learning projects on your resume:

• CNN-based Image Classifier (PyTorch) | 500+ stars
  Implemented ResNet50 architecture for multi-class image classification – Achieved 95% accuracy on a custom dataset of 100,000 images
  github.com/username/image-classification

• Sentiment Analysis using BERT (TensorFlow) | 300+ stars
  Fine-tuned BERT model for sentiment analysis on product reviews – Deployed model as a REST API using Flask and Docker
  github.com/username/nlp-sentiment-analysis

• Stock Price Prediction (Prophet, LSTM) | 250+ stars
  Compared Facebook’s Prophet and LSTM models for stock price forecasting – Implemented backtesting framework to evaluate model performance
  github.com/username/time-series-forecasting

If you’re a student or recent graduate applying for your first machine learning job, you should include any ML projects you completed as part of your degree.

You can list them in a dedicated projects section:

Or in the education section of your resume:

2. Highlight your ML skills and capabilities

Next, you’ll need to list your machine learning skills in the skills section of your resume. Provide a list of relevant technical skills that are required in your target role, including programming languages, frameworks, and tools commonly used in machine learning.

Here is a list of common machine learning skills by category:

Programming and Core ML Libraries

• Languages: Python, R, SQL
• Libraries: NumPy, Pandas, scikit-learn
• Deep Learning Frameworks: TensorFlow, PyTorch, Keras

Machine Learning Algorithms and Techniques

• Supervised Learning: Linear/Logistic Regression, Decision Trees, Random Forests, SVMs
• Unsupervised Learning: K-Means Clustering, PCA
• Deep Learning: CNNs, RNNs (LSTM, GRU), Transformers
• Natural Language Processing: BERT, Word Embeddings, Named Entity Recognition
• Computer Vision: Object Detection, Image Segmentation

Data Processing and Visualization

• Data Cleaning and Preprocessing, Feature Engineering, Exploratory Data Analysis
• Visualization Tools: Matplotlib, Seaborn, Plotly

Big Data and Cloud Technologies

• Big Data: Spark, Hadoop
• Cloud Platforms: AWS (SageMaker), Google Cloud Platform, Azure ML, Distributed Computing

MLOps and Development Tools

• Version Control: Git, GitHub
• Containerization: Docker, Kubernetes, Model Deployment and Monitoring
• Experiment Tracking: MLflow, Weights & Biases
• Development Environments: Jupyter Notebooks, IDEs (PyCharm, VS Code)

3. Write a strong resume summary

Appearing at the top of your resume, your resume summary is a concise statement that highlights your professional experience, key skills, and career goals.

For a machine learning position, focus on your expertise in ML algorithms, relevant experience, and what you aim to achieve in your target role.

Here are some machine learning resume summary examples you can use to make your own:

4. Add numbers to your work experience section

The work experience section of your resume is where employers will check it to see if you have the experience and skills they’re looking for in a machine learning engineer, so getting it right is key. List your ML, data science, or programming roles in reverse chronological order (with your most recent job first). In bullet points below, describe your duties and achievements in each role.

It’s important to quantify your achievements in your resume bullet points with hard numbers so hiring managers understand the scope of your accomplishments and what you’ll be able to contribute if hired.

Here are some examples of work experience bullet points strengthened with hard numbers:

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Average machine learning engineer salaries in the US

After you’ve written your resume, make sure you use it to apply for jobs within a fair salary range.

The following table includes information from the US Bureau of Labor Statistics’ Occupational Employment and Wage Statistics report. Here are the ten states with the highest salaries for machine learning engineers, as well as the national average for your reference when applying for work.