Sr. Machine Learning Engineer, Computer Vision
Pinterestabout 1 year ago
San Francisco, California, United States
Hybrid
Full-time
Junior Level (1-3 years)
Job Description
Position Overview
At Pinterest, we are on a mission to inspire creativity and help millions of people discover new ideas. As part of the Pinterest Labs organization, specifically on the visual modeling team, you will work on developing Pinterest Canvas—an innovative text-to-image generative model that powers advanced visualization features. This role offers a chance to collaborate with experts in applied ML research and development while shaping next-generation generative models.
Key Responsibilities
- Prototype new model architectures for Pinterest Canvas using diffusion text-to-image techniques and hands-on model implementation skills.
- Review research papers, engage in group discussions, and contribute to the overall visual generative strategy.
- Assist in collecting and curating relevant visual training data for initiatives such as RLHF and targeted fine-tuning.
- Publish and publicize your work via conferences, paper submissions, and blog posts.
- Mentor junior researchers and research interns within the Pinterest Labs organization.
Required Qualifications
- Proven experience with generative computer vision models, preferably various forms of diffusion models.
- 5+ years of industry experience in computer vision.
- M.S. or PhD in Machine Learning, Computer Science, or a related field.
- Publications at top machine learning conferences.
Benefits & Perks
- Compensation: Base salary range of $150,713—$310,291 USD with equity eligibility.
- Work Flexibility: Flexible remote work arrangements with in-person collaboration 1-2 times per quarter.
- Inclusive Culture: A workplace committed to equity, inclusion, and inspiring every employee.
- Relocation: This position is not eligible for relocation assistance.
- US based applicants only.
Required Skills
Multimodal Representation Learning
Text-to-Image Synthesis
Research and Publications
Machine Learning
Generative Models
Prototyping Model Architectures
Data Collection
Computer Vision
Diffusion Models
Mentorship