Sr. Machine Learning Engineer, Applied Science

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.

We are currently hiring for the visual modeling team in Labs, which focuses on developing Pinterest Canvas. Canvas is a foundation text-to-image model developed internally for helping various visualization, inpainting, and outpainting products. In this role, you’ll get to work with Pinterest’s rich visual-text dataset to build large-scale generative models which are continuously being shipped to production to power visualization features.

The core Canvas pod is a small group (~6 engineers along with a product prototyping team) inside of Labs, allowing for deep collaboration. For example, engineers working on the Canvas project also participate in the development of our core multimodal text & image embeddings, as these are in turn used to condition the model in unique ways.

• 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.

• Research engineers and scientists who have experience working with generative computer vision models, preferably various forms of diffusion models.

• M.S. or PhD in Machine Learning, Computer Science, or related areas.

• 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.

• We let the type of work you do guide the collaboration style. That means we're not always working in an office, but we continue to gather for key moments of collaboration and connection.

• This role will need to be in the office for in-person collaboration 1-2 times/quarter and therefore can be situated anywhere in the country.