AI Engineer (Full-stack)

Tastelabs; San Francisco (CA); On-site; USD 100,000 - 130,000; Full time

Job summary

Tastelabs is seeking a skilled developer to build AI systems that power taste evaluations, tooling, and data collection. The role involves designing back-end data pipelines and sometimes shipping front-end experiences. You will tackle complex problems related to AI and taste, operating in a high-ownership environment.

The ideal candidate has experience in early-stage startups, a strong understanding of AI systems, and a genuine curiosity for taste. Join us to innovate and create something unique!

Qualifications

• Experience in building AI systems and agent architectures.
• Curiosity about taste and creative problem-solving skills.
• Previous roles in early-stage startups are ideal.

Responsibilities

• Build AI systems that power taste evaluations and tooling.
• Design evaluation pipelines and infrastructure for data collection.
• Create scraping systems for visual data across the web.

Skills

Backend development

Frontend development

AI systems

Synthetic data generation

Creative problem-solving

Job description

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 when needed. Early stage, high ownership, lots of building from scratch.

Types of problems you’ll 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.).