AI/ML Engineer Intern
About the Role
We're looking for a motivated AI/ML intern who's eager to learn, move fast, and build real production systems. This isn't a coffee-fetching internship - you'll work on meaningful projects that ship to production, collaborate with experienced engineers, and gain hands-on experience across the ML lifecycle.
You'll be exposed to greenfield projects, modern ML infrastructure, and real-world problem-solving. We're looking for someone who's curious, scrappy, and comfortable figuring things out independently with guidance from the team.
What You'll Do
- Build and deploy real ML models (not just Jupyter notebooks)
- Contribute to production ML pipelines and infrastructure
- Work with data - cleaning, processing, feature engineering
- Learn MLOps practices - Docker, CI/CD, model deployment
- Collaborate with senior engineers on research and implementation
- Ship code to production (with supervision and code review)
- Experiment with new ML techniques and tools
Required Skills
Core (Must Have)
- Python - Strong programming skills, comfortable writing clean code beyond notebooks
- ML Fundamentals - Solid understanding of machine learning concepts through coursework or personal projects
- Supervised/unsupervised learning
- Model evaluation and validation
- Basic understanding of neural networks
- Data Manipulation - Experience with Pandas, NumPy, or similar libraries
- Version Control - Git basics (commit, push, pull, branches)
- Problem Solving - Can debug issues and search for solutions independently
- Communication - Can explain technical concepts clearly
At Least One of These ML Areas
- NLP/LLMs: Experience with transformers, text processing, or language models (even if just through projects)
- Time Series: Forecasting, sequential data, or time-based analysis
- Recommender Systems: Collaborative filtering or ranking (through projects/coursework)
- Computer Vision: Image classification, object detection, or CNN projects
- Classical ML: Strong foundation in scikit-learn, feature engineering, model selection
Working Style
- Self-starter - You don't wait to be told what to do; you ask questions and take initiative
- Fast learner - Comfortable picking up new tools and technologies quickly
- CLI-comfortable - Not afraid of the terminal (we can teach you more advanced usage)
- Curious - Genuinely interested in how things work under the hood
- Collaborative - Can work with a team and ask for help when stuck
Nice to Have
- Cloud Experience: Any exposure to AWS, Azure, GCP, or OCI
- Docker/Containers: Basic understanding of containerization
- SQL: Database querying experience
- CI/CD: Familiarity with GitHub Actions, Azure DevOps, or Jenkins
- MLOps Tools: Exposure to MLflow, Weights & Biases, or Airflow
- Bash/PowerShell: Basic scripting skills
- Deep Learning Frameworks: PyTorch or TensorFlow experience
- Kaggle Competitions: Participated in ML competitions
- Open Source: Contributions to projects or active GitHub profile
- Additional Languages: Any experience with Go, Rust, or JavaScript
What We'll Teach You
- Production ML deployment and monitoring
- MLOps best practices and tools
- Cloud-native AI/ML services across multiple providers
- Building scalable data pipelines
- Advanced CLI workflows and productivity tools
- Code review and software engineering practices
- Working in a professional engineering environment
Our Stack:
Core: Python | PyTorch/TensorFlow | Scikit-learn | FastAPI/Flask | Git | Bash/PowerShell
ML/AI Tools: MLflow | Airflow/Kubeflow | Azure AI | AWS SageMaker/Bedrock | GCP Vertex AI | OCI AI Services
Infrastructure: Docker | Kubernetes | AWS/Azure/GCP/OCI | PostgreSQL | Azure DevOps | GitHub Actions
Education & Experience
- Currently pursuing Bachelor's or Master's in Computer Science, Data Science, Machine Learning, or related field
- Minimum GPA of 3.0 preferred
- Completed coursework in machine learning, statistics, or data science
- Personal projects, internships, or research experience in ML/AI is a plus
Internship Details
- Duration: 3-6 months (flexible based on school schedule)
- Type: Full-time during summer, part-time during semester possible
- Location: Hybrid
- Compensation: Competitive hourly rate based on education level and experience
- Mentorship: Assigned mentor + regular 1:1s with senior engineers
- Projects: Real production work, not busy work
Why Intern With Us
- Ship real code: Your work will be used by actual users
- Learn from the best: Work directly with experienced ML engineers
- Modern stack: Get hands-on with cutting-edge AI/ML tools
- Multi-cloud exposure: Learn AWS, Azure, GCP, and OCI
- Fast-paced: Just build and learn
- Potential for full-time: Strong interns often receive return offers
Interview Process
- Phone screen (30 min) - background, interests, and basic technical questions
- Coding challenge (take-home, 2-3 hours) - solve a real ML problem
- Technical interview (45 min) - discuss your project and ML fundamentals
- Behavioral interview (30 min) - culture fit and working style
- Team meet (optional, 20 min) - meet potential teammates
To Apply: Send your resume and include:
- A brief description of your most interesting ML project (personal, academic, or work)
- Link to your GitHub or portfolio (if available)
- Which ML area(s) from our list interest you most and why
- Your available start date and duration
Employment Type
Intern
Job Location
Austin, Texas, 78741, United States
Date posted
February 16, 2026