Research Engineer, Lead

San Francisco, CA · In Person · Full-Time

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.

About Vizcom

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 professional judgment they bring to the decision. Existing approaches don't cleanly separate those signals.

Understanding that judgment — and learning how to model it — is the challenge this role will help solve.

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.

This role sits directly between research and product. You'll have the opportunity to see the models you work on ship to working designers, while learning from the real-world signals generated by how those designers use Vizcom.

If your primary goal is research that ends with publication, this may not be the right environment. If you're excited by the idea of helping build models that influence what professional designers see in the product, it probably is.

We also believe the strongest results won't come from clever objectives alone. They'll come from excellent engineering: correct training code, rigorous evaluations, reliable pipelines, and experiments we can trust.

What You'll Own

Execute well-scoped post-training research and engineering projects, from experiment design through evaluation and implementation.

Train and evaluate reward and preference models using years of professional design decisions.

Explore and apply methods including supervised fine-tuning, distillation, preference optimization, and reinforcement learning.

Develop rigorous evaluations that help us determine whether an experimental result is real, reproducible, and worth pursuing.

Work closely with more senior research and engineering partners to translate promising research results into production systems.

Partner with Product and Design to understand how models perform in real workflows and identify opportunities to improve them.

Evaluate emerging post-training techniques and prototype approaches that may be useful within our stack.

Contribute to reliable training and experimentation infrastructure that makes it easier to run, compare, and reproduce experiments.

Document experiments, results, and learnings so the team can build on them over time.

This is a charter, not a week-one checklist. We don't expect one person to tackle everything at once. You'll work with the team to prioritize the problems where you can have the most impact.

What Your First 90 Days Could Look Like

Days 1–30: Learn and map

Understand our data, post-training stack, existing research, evaluation methods, and the approaches we've already tested. Get comfortable running experiments within the existing training and evaluation infrastructure.