Master's Machine Learning Internship 2027 (USA)
About this role
Graduate students who thrive on tackling complex algorithmic challenges and want hands-on access to massive datasets will find a natural home in this program. Pinterest engineering teams build large-scale recommendation systems and personalized experiences for hundreds of millions of users around the globe. During the 12-week program, interns take ownership of a dedicated project from start to finish, working alongside experienced researchers and engineers to deliver production-ready machine learning solutions.
This opportunity exists to bring fresh academic perspectives into the company while helping the next generation of technologists bridge the gap between theory and industrial scale. Successful candidates demonstrate strong foundational knowledge in machine learning, mastery of systems programming, and a high degree of autonomy in solving open-ended analytical problems.
What you will do
- Lead an individual project from inception to completion to apply machine learning and artificial intelligence research to practical platform challenges.
- Gain practical experience across the full production machine learning lifecycle, including algorithmic research, infrastructure, data engineering, training, inference, and product delivery.
- Leverage frontier artificial intelligence tools and agents to accelerate prototyping, experimentation, and implementation work.
- Validate outputs generated by AI through rigorous testing, code review, and critical thinking to maintain security, accuracy, and maintainable standards.
- Utilize AI tools to investigate bugs, comprehend unfamiliar codebases, and synthesize technical documentation.
- Write clean, efficient, and sustainable code while maintaining proactive ownership of assigned tasks and project quality with guidance from mentors and peers.
What you need
- Currently working toward a Master's degree in Computer Science, Machine Learning, Natural Language Processing, Statistics, Information Sciences, or a related technical field.
- Demonstrated knowledge in machine learning areas such as ranking, computer vision, natural language processing, content recommendations, embeddings, or information retrieval.
- Mastery of at least one systems programming language including Java, C++, or Python.
- Proficiency in at least one machine learning framework such as TensorFlow, PyTorch, or MLFlow.
- Familiarity with AI-native engineering principles, including the design of agent-friendly codebases and agent-first development tools.
- Strong critical thinking skills to evaluate AI-generated suggestions for performance, correctness, security, and maintainability.
- Demonstrated experience conducting research, solving analytical problems, and communicating effectively as a team player.
Benefits
- Paid 12-week internship program
- Mentorship from experienced engineers and researchers
- Participation in professional development workshops
- Networking events to build career connections
Skills and tech stack
Questions candidates ask
What are the remote and office location policies for this internship?
Depending on the team, summer internships will be located either remote or hybrid in San Francisco, Palo Alto, New York, or Seattle offices. The role may require you to be located near an office for in-person collaboration and a commutable distance from one of the company locations.
What is the compensation for this internship?
The salary for this position is $11,000 monthly. Final salary is based on a number of factors including location, travel, relevant prior experience, or particular skills and expertise.
What are the start dates and duration of the internship?
Internships are 12 weeks paid, with fixed start dates in May or June to accommodate varying school calendars. The company offers two start dates: May 17, 2027 to August 6, 2027, or June 14, 2027 to September 3, 2027.
What educational background is required to apply?
Candidates must be working towards a Master's degree in Computer Science, Machine Learning, Natural Language Processing, Statistics, Information Sciences, or a related field.