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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 youâd work on
Research different grading methods and rubrics
Design unique tasks that can capture elements of âtasteâ 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
Youâre a team player and have startup DNA: you move fast, adapt, no such thing is ânot in scopeâ, you like to take ownership of things. Youâll 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.)
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