- Location
- Zurich, CH · Zurich
- Type
- Internship
- Department
- Engineering
- Seniority
- Internship
- Education
- PhD
Description
About Pinterest:
Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.
Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the flexibility to do your best work. Creating a career you love? It’s Possible.
At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.
Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here.
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.
What you’ll do:
- 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.
What we are looking for:
- Working towards a Master's or PhD degree in Computer Science, ML, NLP, Statistics, Information Sciences or related field
- 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
In-Office Requirement:
- This onsite internship will be based in our Zurich office requiring a few days in office. Our program will run in a full-time capacity during typical business hours between Monday-Friday.
- This role will need to be in the office for in-person collaboration 2-3 weekdays per week and therefore needs to be within a commutable distance of Zurich.
Relocation Statement:
- This position may be eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.
- Information regarding the culture at Pinterest and benefits available for this position can be found here.
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Our Commitment to Inclusion: