Machine Learning Engineer (Computer Vision Applied Science)

Our visual modeling team is currently seeking new members to focus on the advancement of vision-centric LLMs. We are building VLMs capable of perceiving intricate visual details and understanding user aesthetics to facilitate communication through visual assets using tools like multimodal search and text-to-image models This role offers the opportunity to work with Pinterest’s unique visual-text datasets to develop large-scale generative models for production. You will join the core visual pod, a collaborative group of approximately six engineers and a product prototyping team, to create specialized evaluation benchmarks and contribute to the broader research community Prototype new model architectures for Pinterest VLMs. We’re looking for hands-on experience working with finetuning open-source LLM models and improve their visual perception and tool using capabilities Develop new evaluation benchmarks that tailors to vision-centric capabilities such as fashion style recommendations 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 2+ years of industry computer vision experience Research engineers and scientists who have experience working with generative computer vision models, preferably various forms of visual encoders and LLMs M.S. or PhD in Machine Learning, Computer Science, or related areas Publications at top ML conferences Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring