AI Engineer
Taste Labs
AI Engineer
Taste Labs
Design taste data for training AI models
About the company
Taste Labs is building the data and infrastructure layer for taste.
Our goal is to end AI slop. To make AI feel right, not just be correct. We raised $18.5M in seed co-led by Amplify and CRV, and most frontier labs are already customers.
AI has nailed objective domains and can generate anything. The hard part left is judgement: what fits, what feels like you, what's actually GREAT. We're turning that into something measurable, starting with design.
We do it on two sides: building the post-training data and RL environments that teach taste to frontier models, and the context and verification tools agents need to produce work that's more creative, more on-brand, more right.
If that problem excites you, you'll like it here!
About the role
How do you judge something non-verifiable, like design? To solve subjective domains, we have to solve how to grade the difference between slop and great without the need for humans in the loop.
Types of problems you’d work on
Research different grading methods and rubrics
Design unique tasks that can capture elements of “taste” and design capabilities
Build agent harnesses and context layers
Work on scalable RL infra
Work with internal research teams on our training pipelines
Work with the top frontier labs on how to craft environments to improve frontier models
What matters to us
Experience in building evals, RL environments, ML or post-training. Strong backend experience as well.
You like ambiguous, hard, creative problems, and want to make subjective domains verifiable
You’re a team player and have startup DNA: you move fast, adapt, no such thing is ‘not in scope’, you like to take ownership of things. You’ll be given a lot of room to run.
Bonus points
Open source contributions or personal projects that show you build things because you're curious
Background at creative companies (Figma, Notion, Canva, Adobe, Runway, etc.) or companies with strong index building/crawling (e.g. Firecrawl, Brave, Luma, Pika) or data (Mercor, Surge, etc.)
Design taste data for training AI models
Taste Labs builds the data and infrastructure layer that AI systems use to judge subjective quality, starting with design. The company measures, classifies, and codifies aesthetic judgment into data models that systems can train on, then supplies the context, evaluation, and verification tooling that sits on top. It works with frontier labs on post-training to improve taste and design capability, and with app developers, coding agents, and creative-tool companies checking the design work their models produce. Unlike benchmarks that score correctness, it targets the harder question of whether output is good, on-brand, or right for a particular person. The company's goal is to give models reliable judgment by turning taste into training data instead of leaving it to prompt engineering.
Explore more opportunities at Taste Labs