Machine Learning Intern 2027 (Zurich)

Overview

Pinterest is offering a Machine Learning internship in Zurich through its University Engineering organisation. Interns apply machine learning and AI to product and engineering problems, working with engineering, product and data teams to prototype, evaluate and improve ML-powered features and discovery experiences. They contribute to real projects, learn how large-scale ML systems are built and develop practical skills with guidance from experienced engineers.

What you will do

Each intern owns a scoped machine learning project, from defining the problem through experiments and implementation to presenting results, with support from a manager and team. Other tasks include contributing to applied ML and AI research that informs Pinterest product experiences, collecting and analysing data to build models, writing clean and maintainable code with documentation and tests, and applying techniques such as natural language processing or graph-based approaches to modelling and ranking problems across discovery, ads and search. Interns break down technical problems, propose approaches, deliver project milestones and share progress with teammates. AI tools may be used to speed up analysis, provided results are verified for correctness and quality.

Who can apply

Applicants should be working toward a Master's or PhD in Computer Science, machine learning, NLP, statistics, information sciences or a related field.

Requirements

The employer looks for experience in areas such as ranking, computer vision, NLP, content recommendations, embeddings or information retrieval, plus experience with big data technologies such as Hadoop or Spark and scalable real-time systems that process streaming data. Proficiency in at least one systems language (Java, C++ or Python) or one ML framework (TensorFlow, PyTorch or MLflow) is expected, along with research experience, analytical problem solving and strong communication and teamwork on open-ended problems.

The internship is full time on weekdays and based in the Zurich office, with in-person collaboration two to three weekdays a week, so candidates need to be within commuting distance. Relocation assistance may be available.