About Glue
Glue is not just another team chat app. We're building the first platform for agentic team chat—a workspace where AI agents and humans collaborate as peers. Our platform supports leading AI models (GPT-5, Claude, Gemini, and open-source alternatives) and integrates with thousands of apps through the Model Context Protocol (MCP), enabling teams to direct actions across their entire tech stack without leaving chat.
We recently raised $20M in Series A funding. Co-founded by David Sacks and Evan Owen, Glue is redefining how teams communicate and get work done in the AI era.
Opportunity
We’re looking for an AI/ML Engineer with a strong foundation in large language models (LLMs) and retrieval-augmented generation (RAG) to help us build smart, useful, and scalable AI features. You’ll work across the stack—from prototyping prompts and pipelines to shipping production systems—and play a key role in evaluating model performance and behavior. You'll collaborate across engineering, product, and design to ship experiences that feel magical—but are grounded in robust infrastructure and measurable outcomes.
About You
Have shipped across the stack to production—from data ingestion and model integration to serving and monitoring—and understand what it takes to build resilient ML-powered features.Have dealt with issues like latency, cost, edge cases, and prompt brittleness, and you’ve built systems that people rely onUnderstand the nuances of RAG systems, including text chunking, hybrid retrieval (semantic + keyword), vector store tuning, and relevance optimization.A self-starter, a builder, a doer. You know how to architect a project the “right way”, but also know when to ask about requirements and make tradeoffs depending on the maturity of the projectCare about details but know when to avoid getting lost in the weeds and prioritize the immediate outcomes needed.Want to be part of a scrappy, early team who is building something ambitious and exciting.
What will help you succeed
5+ years of engineering experience, ideally including ML, NLP, or applied AI experience2+ years hands-on with LLMs, transformers, and production-grade generative AI systems2+ years of experience with a server-side language such as Go, TypeScript, Python, etc.Experience building RAG systems using frameworks like LangChain, LlamaIndex, or custom stacksExperience with multi-modal inputs, fine-tuning open models, or orchestrating agent-style systemsFamiliarity with evaluation techniques, tooling (e.g., Braintrust, Promptfoo, etc.) and quality metricsIndependent and self motivated—maintaining side projects or libraries a major plus
