Backed by a16z.
About the role
Architect and build Krea’s personalization and recommendation stack from the ground up, owning the technical direction end to end Design algorithms to model user preference and taste, enabling Krea’s models to adapt to individual styles and aesthetics
What they're looking for
- Strong experience building recommendation systems or personalized feeds from scratch
- Proven ability to design and ship high-quality curated content experiences
- Experience working with media-based personalization (image, video preferred, music or other modalities also welcome)
- Solid foundations in machine learning, representation learning, and modern deep learning techniques
- Strong Python skills and experience with ML frameworks such as PyTorch or JAX
- Ability to operate independently, make architectural decisions, and own complex systems end to end
More about this role
At Krea, we are building next-generation AI creative tools.
We're dedicated to making AI intuitive and controllable for creatives - our mission is to build tools that empower human creativity, not replace it. We believe AI is a new medium that allows us to express ourselves through various formats - text, images, video, sound, and even 3D. We're building better, smarter, and more controllable tools to harness this medium. We recently took this a step forward with the launch of Krea 2 , our first foundation model, built completely from scratch for aesthetic diversity and stylistic control.
We've raised over $83M and are backed by world-class investors such as a16z, Bain Capital, and Abstract. We work full-time and in-person at our waterfront office in San Francisco. We care about creativity: our team includes musicians, designers, visual artists, and engineers.
We’re looking for an ML Engineer to architect and build Krea’s personalization and recommendation systems from scratch. You’ll have full ownership over how we understand user taste, curate content, and adapt generative models to individual aesthetics.
This is a role at the intersection of recommendation systems,...
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