AI-First Full-Stack Engineer: Ship Real-World AI

Acelab Inc.; New York (NY); Hybrid; USD 140,000 - 210,000; Full time

Job summary

Acelab Inc. in New York, NY hybrid is building AI-powered materials platform for architects and designers. The role gives ownership from API to UI and tasks you to ship end-to-end systems with AI at the core.

You will design and ship AI-enabled features, data pipelines, and scalable APIs on a small, fast-moving team where your work shapes a new product category and has visible impact.

Qualifications

• Shipping production software to users with real impact.
• Strong command of frontend (TypeScript/React) and backend (Python/Node.js).
• Deep, practical experience with LLMs, evals, RAG pipelines and prompt engineering.
• Experience with ETL, vector databases, and structured/unstructured data at scale.
• Ability to define scope in ambiguous situations and move fast.

Responsibilities

• Own AI-powered search and recommendation systems, including data structures, LLM integration, vector search, and feedback loops.
• Develop full-stack features in TypeScript/React and Python/Node.js to support complex workflows.
• Design data pipelines to ingest, normalize, and enrich product data from thousands of manufacturers.
• Shape API design and platform infrastructure for a growing partner ecosystem.

Skills

TypeScript/React

Python or Node.js

LLMs & AI systems

Data engineering fundamentals

ETL

Vector databases

Job description

New York, NY Hybrid · Full Time · Includes technical challenge

What We’re Building

Every building around you represents hundreds of material decisions. Architects and designers drive $420B+ of those decisions annually in the US alone, and the tools they use to make them are broken. Acelab is the platform fixing that: structured material data, AI-powered workflows, and seamless collaboration between the people who specify and the manufacturers who supply.

We're not a research project. We're in production, growing fast, and we need people who can ship.

The Role

You'll own the product and infrastructure end-to-end—from the API layer to the user interface—on a team where AI is not a feature but the foundation. You'll design and ship systems that make complex workflows feel simple for architects and designers, and you'll build AI into the product from first principles, not as an afterthought.

We're a small, fast-moving team. Your decisions carry weight and your output is visible. If you want to spend a year improving a dashboard, this isn't the place. If you want to shape a product category, it is.

What You’ll Work On

• AI-powered search and recommendation: design and own the systems that help architects find and evaluate materials. That means structured data, LLM integration, vector search, and feedback loops that improve over time.
• Full-stack product features: build interfaces in TypeScript/React that make complex workflows feel effortless. You think in components, states, and users simultaneously.Data pipelines and ingestion: architect reliable systems to ingest, normalize, and enrich product data from thousands of manufacturers at scale.
• API and platform infrastructure: design the APIs and integrations that power our partner ecosystem. You think about contracts, versioning, and extensibility from day one.

Why Acelab

Real ownership. You'll ship features that architects and designers use every day. No roadmap theater.AI at the center. We use AI internally and build it into the product. You'll be surrounded by people who think seriously about it — not just hype it.Early-stage leverage. We're small and growing fast. The systems you design now will define how we scale.Competitive compensation: salary, equity, and benefits commensurate with experience.

What We’re Looking For

Experience shipping production software to users. You've owned features end-to-end and have the users to prove it.Strong command of TypeScript/React on the frontend and Python or Node.js on the backend. You're comfortable context-switching between layers.Deep, practical experience with LLMs and AI systems—not just API calls, but prompt engineering, evals, RAG pipelines, and knowing when AI is the right tool and when it isn't.Experience with data engineering fundamentals: ETL, vector databases, structured and unstructured data at scale.Comfort with ambiguity. You don't need a perfect spec; you define scope, ask the right questions, and move.You read the docs instead of just asking ChatGPT—and you know when to do which.

What Makes Someone Stand Out

You've built something with real users and real stakes: a product, a startup, an open-source tool. You can talk through the decisions you made and why.You've worked with large-scale data systems: pipelines, vector search, APIs, and the tradeoffs that come with scale.You think in systems. You build things that are easy to extend, not just things that work once.You have genuine curiosity about the built environment, data markets, or B2B SaaS — you care about the domain, not just the stack.