AI Engineer (Back-end)

You’ll build the AI systems that power taste agent harnesses, memory, evals, tooling, data collection, index, search, graph, crawling, scraping, security and more.

Types of problems you’d work on

• Craft agent harnesses, memory and self-improvement loops
• Design evaluation pipelines and synthetic data generation
• 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
• Manage security sweeps vulnerability mapping
• Develop the tooling and infrastructure that makes everything reliable and fast

What matters to us

• Deeply technical: you’re deeply opinionated about what good code means, you have an AI-forward setup while being anti-slop, you care deeply about the craft.
• You’re creative and have genuine curiosity about ‘taste’ and making subjective domains verifiable
• You have startup DNA: you move fast, adapt, no such thing as ‘not in scope’, you like ambiguous, hard problems
• AI experience: you need experience building AI forward products, systems, agents, models and more.
• You care about arriving at the simple, clear solution vs. the complex one

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.)

Compensation Range: $175K - $275K

Do you have experience in AI tools proficiency?

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.