Senior Staff Machine Learning Scientist, Assets

At Webflow , we’re building the world’s leading AI-native Digital Experience Platform, and we’re doing it as a remote-first company built on trust, transparency, and a whole lot of creativity. This work takes grit, because we move fast, without ever sacrificing craft or quality. Our mission is to bring development superpowers to everyone. From entrepreneurs launching their first idea to global enterprises scaling their digital presence, we empower teams to design, launch, and optimize for the web without barriers. We believe the future of the web, and work, is more open, more creative, and more equitable. And we’re here to build it together.

We’re looking for a Senior Staff Machine Learning Scientist to help us solve challenging problems to address emerging customer needs and behaviors. The ideal candidate can move fast but with high quality, and views the early stages of product development as a creative canvas and opportunity for high impact.

About the role:

• Location: Remote-first (United States; BC & ON, Canada)
• Full-time
• Permanent
• Exempt
• Our cash compensation amount for this role ranges depending on the cost of labor of the geographic area. The ranges shared below may change if you are hired in another geographic location.
• Zone A: 220,000 - 285,000
• Zone B: 207,000 - 268,000
• Zone C: 194,000 - 251,000
• 251,000 - 324,000

Please visit our Careers page for more information on which locations are included in each of our geographic pay zones. However, please confirm the zone for your specific location with your recruiter.

• Application deadline: applications accepted on an ongoing basis until position is closed and filled
• This posting is for a new position
• Reporting to the Director, Engineering

As a Senior Staff Machine Learning Scientist, you’ll:

• Lead and drive ambitious research initiatives that advance the state of the art in computer vision, multimodal understanding, and visual generation.
• Develop novel models, algorithms, and training methodologies for challenging vision problems such as image understanding, video understanding, visual search, scene representation, segmentation, detection, generation, and multimodal reasoning.
• Translate cutting-edge research into practical model improvements that can shape product direction and unlock new user experiences.
• Design, implement, train, and optimize large-scale vision and multimodal foundation models across diverse datasets and tasks.
• Partner closely with applied scientists, ML engineers, and product teams to move research from exploration to production-ready systems.
• Set technical direction for high-impact research areas, identifying promising bets and influencing longer-term strategy for vision and multimodal AI.
• Mentor other scientists and engineers, raise the quality bar for research,