Description
At Pinterest, we're on a mission to bring everyone the inspiration to create a life they love. We're looking for a Sr. Machine Learning Engineer to join our Applied Science team. As a Sr. Machine Learning Engineer, you will be responsible for developing and deploying machine learning models to improve the core product.
The ideal candidate will have a strong background in machine learning, experience with large-scale model deployment, and excellent communication skills. You will work closely with cross-functional teams to design and implement new features and improve existing ones.
Responsibilities:
- Prototype new model architectures for Pinterest Canvas, our internal text-to-image generative model.
- 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.
Requirements:
- 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.
Nice to Have:
- 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.
In-Office Requirement Statement:
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.
Relocation Statement:
This position is not eligible for relocation assistance.
Visit our PinFlex page to learn more about our working model.
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