Machine Learning Intern

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