Material Bank is the world’s largest material marketplace for the architecture and design industry. Operating in 37 countries, our platform has become the standard for design professionals around the globe. Every day, Material Bank connects thousands of designers with tens of thousands of materials from leading brands. Material Bank is the fastest and most powerful way for design professionals to search, sample, and specify materials.

About the role

As an Applied AI Engineer, you will drive the design, build, and deployment of next-generation AI-powered experiences across Material Bank’s platform. You will work as part of the team responsible for taking ideas from concept to production — building intelligent systems and user experiences that blend cutting-edge AI capabilities with the high standards of quality, aesthetics, and usability expected in the architecture and community.

This is a senior-level individual contributor role focused on applied AI product development. You will work across the stack to architect and deploy scalable AI systems that enhance how users discover, understand, and engage with products, materials, and creative content. Your work will span areas such as multimodal search and understanding, AI-assisted content generation, intelligent workflows, personalization, creative tooling, and agentic systems.

We are looking for someone who not only understands modern AI systems technically, but also has strong product instincts, visual sensibility, and genuine passion for building AI experiences that feel thoughtful, polished, and useful. AI will play a foundational role in the future of Material Bank’s platform, and you will help define and build that future.

What you’ll do

Design, build, and deploy end-to-end AI-powered product experiences from concept through production.
Architect and implement scalable AI systems leveraging LLMs, embeddings, multimodal models, retrieval systems, agent frameworks, and modern data infrastructure.
Build production-grade multi-agent workflows and orchestration systems using frameworks such as LangGraph, LangChain, Mastra, and custom tooling.
Develop and optimize Retrieval-Augmented Generation (RAG) systems, including embeddings, vector search, retrieval pipelines, chunking strategies, and relevance tuning.
Build multimodal AI workflows that analyze and reason over images, creative assets, and visual datasets using modern multimodal LLMs, embedding models, and specialized tooling such as SAM2/SAM3.
Create AI-assisted experiences for search, discovery, content generation, personalization, and creative workflows across Material Bank’s platform.
Evaluate, refine, and improve AI-generated outputs for quality, tone, accuracy, and creative alignment through testing, iteration, and human-in-the-loop evaluation strategies.
Partner closely with Product, Design, Engineering, Data, and Executive Leadership to identify high-impact opportunities and translate ambiguous ideas into production-ready AI capabilities.
Make architectural decisions that balance speed, scalability, latency, cost, accuracy, and long-term maintainability.
Continuously evaluate emerging AI technologies, models, frameworks, and workflows to identify opportunities that create meaningful business and user value.

What you’ll bring

8+ years of experience building and shipping production software, including significant full-stack engineering experience.
Demonstrated success designing and deploying production-grade AI/ML systems and AI-powered product experiences.
Deep hands-on experience with LLMs, embeddings, multimodal AI systems, RAG architectures, and multi-agent frameworks such as LangGraph, LangChain, Mastra, or equivalent custom tooling.
Strong engineering fundamentals across backend systems, APIs, data pipelines, cloud infrastructure, and modern JavaScript/TypeScript and Python ecosystems.
Experience working with multimodal models, visual analysis systems, and image-based AI workflows at scale, including familiarity with modern image-generation tooling and services.
Strong systems thinking with the ability to balance trade-offs across latency, cost, scalability, accuracy, reliability, and user experience.
Proven ability to independently take ambiguous problems from idea to shipped product with minimal oversight.
Strong product instincts, visual sensibility, and a high bar for quality, usability, and craftsmanship in AI-generated experi

Applied AI Engineer at Material Bank