AI ML Intern Jobs
AI ML Intern jobs are open across technology, healthcare, finance, and research institutions, at levels from undergraduate to recent graduate, with specializations in computer vision, natural language processing, and predictive modeling. See the openings below and apply to the ones that match your experience.
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Showing 5 of 180+ AI ML Intern jobs
INTRODUCTION
Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
BASIC QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10+ years of engineering experience, including 3-5+ years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance)
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering
PREFERRED QUALIFICATIONS
- Familiarity with the financial services industries
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
INTRODUCTION
Provide hands-on technical leadership by designing, developing, and deploying ML/LLM/GenAI solutions from concept through production, maintaining ownership for reliability and operability once deployed. Work closely with product managers, data scientists, ML engineers, and other stakeholders to understand requirements and prioritize use cases. Mentor and uplift junior engineers through design reviews, code reviews, pairing, and coaching, raising engineering quality and delivery discipline across the team. You will build and institutionalize MLOps capabilities, including automated pipelines for deployment, monitoring, and model lifecycle management, with emphasis on scalability and reliability. Implement optimization strategies to fine-tune generative models for specific NLP use cases, ensuring high-quality outputs in summarization and text generation. Conduct thorough evaluations of generative models (e.g., GPT-4.1), iterate on model architectures, and implement improvements to enhance overall performance in NLP applications. Implement monitoring mechanisms to track model performance in real-time and ensure model reliability. Communicate AI/ML/LLM/GenAI capabilities and results to both technical and non-technical audiences. Stay informed about the latest trends and advancements in the latest AI/ML/LLM/GenAI research, implement cutting-edge techniques, and leverage external APIs for enhanced functionality.
BASIC QUALIFICATIONS
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field
- 10+ years of engineering experience, including 3-5+ years building, deploying, and operating applied AI/ML systems in production (model lifecycle, MLOps, monitoring, and governance)
- Demonstrate hands-on engineering leadership: setting technical direction, making architecture decisions, conducting design and code reviews, mentoring junior engineers, and guiding implementation quality across multiple workstreams
- Proficiency in programming languages like Python for model development, experimentation, and integration with OpenAI API
- Experience with machine learning frameworks, libraries, and APIs, such as TensorFlow, PyTorch, Scikit-learn, and OpenAI API
- Experience with cloud computing platforms (e.g., AWS, Azure, or Google Cloud Platform), containerization technologies (e.g., Docker and Kubernetes), and microservices design, implementation, and performance optimization
- Solid understanding of fundamentals of statistics, machine learning (e.g., classification, regression, time series, deep learning, reinforcement learning), and generative model architectures, particularly GANs, VAEs
- Ability to identify and address AI/ML/LLM/GenAI challenges, implement optimizations and fine-tune models for optimal performance in NLP applications
- Strong collaboration skills to work effectively with cross-functional teams, communicate complex concepts, and contribute to interdisciplinary projects
- A portfolio showcasing successful applications of generative models in NLP projects, including examples of utilizing OpenAI APIs for prompt engineering
PREFERRED QUALIFICATIONS
- Familiarity with the financial services industries
- Expertise in designing and implementing pipelines using Retrieval-Augmented Generation (RAG)
- Hands-on knowledge of Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies
AI ML Intern Jobs by Experience Level
Top Cities Hiring AI ML Interns
Explore AI ML intern openings in the cities hiring most right now.
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Who's Hiring
- JPMorganChase23
- Optum10
- Booz Allen Hamilton9
- General Motors (GM)9
- Amazon Web Services8
Top Industries Hiring
- Technology & Software21
- Banking & Financial Services14
- Electronics & Hardware10
- Consulting & Professional Services9
- Investment & Asset Management7
What Employers Look For
The qualifications that appear most often in AI ML intern jobs.
- Proficiency in Python and familiarity with ML libraries such as TensorFlow, PyTorch, or scikit-learn
- Current enrollment in or recent completion of a bachelor's or master's degree in computer science, data science, or a related field
- Experience building and evaluating supervised or unsupervised machine learning models
- Solid grounding in linear algebra, probability, and statistics as applied to model development
- Ability to work with structured and unstructured datasets using tools like pandas, NumPy, or SQL
- Familiarity with version control using Git and experience sharing reproducible code or notebooks
Tips for Your AI ML Intern Job Search
Tailor your resume to each posting
AI ML intern job descriptions vary sharply between teams. One role wants PyTorch and transformer architectures, another wants scikit-learn and tabular data pipelines. Swap your highlighted tools and project descriptions to mirror the exact stack each posting names.
Showcase a project with real data
Hiring managers for AI ML intern roles want proof you've moved beyond tutorials. Include at least one project trained on a real or public dataset, with a GitHub link showing your data prep, model training, and evaluation steps laid out clearly.
Target teams, not just company names
Large companies often have dozens of ML teams hiring simultaneously. Search for the specific team or product area, such as recommendation systems, fraud detection, or computer vision, so your application materials speak directly to that team's problems.
Apply early to roles that fit
Migrate Mate lists ai ml intern openings from across the United States in one place, so you can find roles that match and apply directly to each listing.
Prepare for a technical screen on fundamentals
Most AI ML intern interviews include a session testing probability, linear algebra, and basic ML concepts alongside coding. Review gradient descent, bias-variance tradeoff, and cross-validation so you can explain the math, not just run the library call.
Follow up with a specific question after interviews
After your technical interview, send a brief follow-up that references one problem or dataset discussed in the session. It signals genuine curiosity about the team's work and keeps your application distinct from candidates who send only a generic thank-you.
AI ML Intern Jobs: Frequently Asked Questions
Which companies are hiring the most ai ml interns?
The most active employers for ai ml interns right now are JPMorganChase, Optum, and Booz Allen Hamilton, and the most openings are in California, Virginia, and Texas, based on current listings on Migrate Mate as of August 2026. Roles are concentrated in technology and research-intensive industries, though healthcare and financial services teams are hiring steadily as well.
How many ai ml intern jobs are remote?
About 51% of ai ml intern openings are fully remote or hybrid as of August 2026, which is higher than average for internships overall. Sub-areas like NLP research, data labeling pipelines, and model evaluation tend to offer the most remote flexibility, while computer vision and robotics roles are more likely to require on-site access to hardware or specialized equipment.
How do you become a ai ml intern?
Start by building a foundation in Python and core ML libraries through coursework or self-study, then apply that knowledge to a complete project you can share publicly. Pursue coursework in statistics and linear algebra to handle technical interview questions confidently. Once your resume includes at least one end-to-end project, apply to openings that match your current skill set and state your availability clearly.
Can you get an ai ml intern job with little or no experience?
You can land an ai ml intern role without prior industry experience if your resume shows a completed hands-on project using a real or public dataset. Recruiters for intern roles expect limited professional history, so a well-documented Jupyter notebook, a Kaggle competition entry, or a school research project with clear results carries real weight. Targeting smaller companies or research labs often lowers the bar further.
What does the ai ml intern interview process look like?
Most AI ML intern processes begin with a recruiter call, followed by a technical screen covering Python coding and ML fundamentals such as loss functions, regularization, and model evaluation metrics. A take-home assignment or live coding session often comes next, asking you to build or debug a model on a provided dataset. Final rounds typically involve a conversation with the team about your past projects and how you approach problem decomposition.
Where can I find and apply to ai ml intern jobs?
You can find and apply to ai ml intern jobs on Migrate Mate, which lists current openings from across the United States. Search for roles that match your skills and preferred location, then apply directly to each listing that fits. New positions are added regularly, so checking back often gives you the best chance of catching freshly posted openings.
See All 180+ AI ML Intern Jobs
Find roles that match your experience and apply in just a few clicks.
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