About the job
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. Within Pinterest, the Pinterest Labs organization focuses on applied ML research and development. Labs focuses on a broad variety of AI/ML initiatives, such as core computer vision, multimodal representation learning, heterogeneous graph neural networks, generative modeling, recommender systems, etc. This is the group that develops foundation ML models that fully leverage the tens of billions of Pins and the associated knowledge graph to improve the core product.
Responsibilities
Prototype new model architectures for Pinterest Canvas, our internal text-to-image generative model. We’re looking for hands-on experience working with diffusion text-to-image models and independent model implementation skills.
Read research papers, participate in group discussions, and help brainstorm our overall visual generative strategy at the company.
Help with collection of relevant visual training data for Pinterest Canvas, particularly to conduct RLHF, targeted fine-tuning, etc.
Publish and publicize your work via conferences, paper submissions, blog posts, etc.
Mentor more junior researchers or research interns within the Pinterest Labs organization.
Qualifications
Minimum
Research engineers and scientists who have experience working with generative computer vision models, preferably various forms of diffusion models.
5+ years of industry computer vision experience.
M.S. or PhD in Machine Learning, Computer Science, or related areas.
Preferred
Publications at top ML conferences.
Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.