Machine Learning Intern 2027 (Zurich)

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.

Responsibilities

• 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

Requirements

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

Benefits

• This position may be eligible for relocation assistance
• In-office collaboration 2-3 weekdays per week