AI-powered design tool for real world product designers and their teams. Go from sketch to photorealistic 3D render in seconds. Start creating for free. Backed by Index and AI Grant.
About the role
As a Research Engineer, Post-Training , you'll work on models that help us understand and learn from the judgment that carries a design from pencil to product. You'll work closely with the engineers building our post-training stack, contributing to experiments, model training, evaluations, and a growing body of research documenting the approaches we've tested, what we've learned, and where we've found meaningful signal.
What they're looking for
- Strong programming and software engineering skills, particularly for machine learning systems
- Hands-on experience training or fine-tuning machine learning models
- Experience with generative models, including diffusion or flow models, through professional work, research, or substantial technical projects
- Familiarity with one or more post-training methods such as supervised fine-tuning, preference optimization, reward modeling, distillation, or reinforcement learning
- Experience designing experiments and evaluating model performance
- Strong fundamentals in machine learning and an ability to turn research ideas into working implementations
More about this role
Applying to this role will also allow us to consider you for other research opportunities at Vizcom. We believe the best roles are shaped around exceptional people, not just job descriptions.
Vizcom is where design teams at companies like Nike, GM, New Balance, and Hasbro bring ideas from sketch to product. Designers use Vizcom to sketch, render, explore color and materials, work in 3D, and prepare concepts for production.
The render itself was never the point. The point is the physical thing that comes after it. We call this pencil to product .
Vizcom is a Series B company with more than $52M raised from investors including Radical Ventures, Index Ventures, and Nat Friedman.
Five years of professional designers working this way has created something difficult to reproduce in a traditional research environment: millions of moments where a trained designer, in the middle of real work, decided what should survive into a product that ultimately has to become real.
Those decisions create a uniquely interesting research problem. A designer's preference among several candidates can reflect the generator's style, where they are in the design process, what they are trying to make, and the...
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