Machine Learning Internship 2027

• As a Machine Learning Intern at Pinterest, you’ll apply machine learning and AI to product and engineering challenges
• You’ll work with engineering, product and data teams to prototype, evaluate and improve ML-powered features and discovery experiences
• Throughout your internship, you’ll contribute to real projects, learn how large-scale ML systems are developed and build practical skills with guidance from experienced engineers
• Own a scoped machine learning project from problem definition through experimentation, implementation and communication of results, with guidance from your manager and team
• Contribute to applied machine learning and AI research that informs Pinterest product experiences
• Collect, analyze and synthesize data to develop data-driven machine learning models
• Write clean, efficient and maintainable code, supported by appropriate documentation and testing
• Apply relevant machine learning techniques, including natural language processing or graph-based approaches, to modeling and ranking problems across discovery, ads and search
• Break down scoped technical problems, propose approaches and implement solutions with guidance from the team
• Deliver assigned project milestones, share progress and findings with teammates and seek guidance when needed
• Experience applying machine learning or AI concepts through research, coursework, projects or internships
• Use AI to accelerate analysis and iteration while applying judgment and verification to ensure correctness and quality

Working towards a Master’s or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related fieldStrong communicator and team player with the ability to find solutions for open-ended problemsMachine Learning (ranking, computer vision, NLP, content recommendations, embedding, information retrieval etc)Proficiency in at least one systems language (Java, C++, Python) or one ML framework (Tensorflow, Pytorch, MLFlow)Experience in research and in solving analytical problemsExperience with big data technologies (e.g., Hadoop/Spark) and scalable realtime systems that process stream data