Pinterest : Hybrid @ {San Francisco, New York, or Seattle}
Pinterest - Hybrid @ {San Francisco, New York, or Seattle} - Full-time + internships
Pinterest’s Advanced Technologies Group (ATG) is an ML applied research organization within the company, focusing on large-scale foundation models (e.g. multimodal encoders, graph representation models, content embeddings, generative models, computer vision signals, etc.) that are deployed throughout the company. ATG is composed primarily of ML engineers and researchers, backed by a strong infrastructure team, and a small product prototyping + design team for deploying new AI/ML features in Pinterest. The organization is highly collaborative, research-driven, and delivers deep impact. The team is hiring for several engineering position
iOS engineer for generative AI products: we are looking for senior or staff iOS engineers who have a track record of building fast prototyping work in the AI space — no deep machine learning domain expertise is required, but the ideal candidate would be comfortable interfacing with our ATG’s ML teams daily. An engineer in this role would be building entirely new features for Pinterest leveraging emerging technologies across LLMs, visual models, recommendation systems, and more.
Computer vision domain specialist: we are looking for researchers or applied engineers with industry experience in the computer vision / visual-language modeling field (e.g. multimodal representation learning, visual diffusion models, visual encoders/decoders, etc.) We encourage the team to regularly publish, and the team works in a highly collaborative, research-driven environment, with full access to the Pinterest image-board-style graph for large-scale pre-training.
Please reach out to me directly (dkislyuk@pinterest.com) if you’re interested in either of these roles.
Additionally, the team is currently hiring for fall 2025 ML research internships for Master’s / PhD students, with opportunities to publish or to work on frontier models in the visual understanding and multimodal representation learning space: https://grnh.se/dad7c60e1us