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
You'll build the AI systems that power taste evals, tooling, data collection, API and RL environments - from agent architectures and data pipelines to the product surfaces where users interact with our platform. The work skews backend (synthetic data, embeddings, crawling, evaluation systems) but you'll also ship front-end tooling and gamified experiences. Early stage, high ownership, lots of building from scratch.
Types of problems you’d work on
Craft agent harnesses, memory and self-improvement loops
Design evaluation pipelines and synthetic data generation
Eval design and grading of unverifiable domains
Create embedding and retrieval infrastructure that scales to millions of requests
Build crawling and scraping systems for visual data across the web
Set up inference serving and APIs for client-facing products
Develop the tooling and infrastructure that makes everything reliable and fast
Ship internal tools for data operations and external tools for expert annotators
Build gamified product experiences: taste quizzes, leaderboards, reward flows
What matters to us
Startup DNA: You've built at early-stage companies (pre-seed to Series C) and operate well in ambiguity
Real AI building experience: You've shipped agent systems, built with LLMs, and understand the craft - whether through your job, open source, or serious personal projects.
Genuine curiosity about taste: This problem is hard, nuanced and undefined. You find that energizing, not frustrating.
Creative problem-solving: We're not optimizing existing systems. We're inventing infrastructure for something that doesn't exist yet.
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