> Markdown version of [/jobs/ext/2710645-ai-engineer](https://www.wearedevelopers.com/jobs/ext/2710645-ai-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Engineer - **Company:** HILBERT AI CO. - **Location:** San Francisco, CA, United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Infrastructure, Recommender Systems, Software Deployment, Software Engineering, Large Language Models, Generative AI, Backend, Api Design - **Published:** September 4, 2026 - **Apply:** https://startup.jobs/ai-engineer-core-hilberts-ai-8144304 ## About the Role * You have real experience with LangChain, LangGraph, or equivalent agent/orchestration frameworks. You've built with them, hit their limits, and worked around them - not just followed tutorials * You communicate with clarity and conviction. You can explain a technical decision to a non-technical founder and debate architecture tradeoffs with a senior engineer . Communication is not a nice-to-have here - it's core to the role * You take ownership. You don't wait for tickets. You see what needs to be built, raise your hand, and ship it * You thrive in ambiguity. AI products evolve fast. Requirements change. You're energized by figuring it out. * You move at startup speed. You understand what it means to be available, responsive, and biased toward action in a fast-moving, early-stage environment Strong pluses: * Experience building evals pipelines - designing metrics, running systematic evaluations, and using results to drive iteration on AI systems * Backend software engineering experience - building APIs, services, data infrastructure, or production systems * Exposure to retrieval-augmented generation (RAG), vector databases, or LLM-powered search and recommendation systems * Experience at early-stage startups or high-growth environments where you wore multiple hats You might be: A backend engineer who went deep on LLMs and never looked back. An ML engineer who realized they love building products, not just models. A startup CTO who wants to go deep on AI at a company where the stack is the product. Someone who's been hacking on agents and pipelines nights and weekends and wants to do it full-time with real enterprise stakes. What matters: you ship, you own it, and you communicate like a teammate - not a silo. ## Description You'll work directly with the founding team and across product, data, and GTM to design, build, and improve the AI systems at the heart of Hilbert. The environment is high-autonomy and high-ambiguity - the nature of building AI-native products means requirements shift, approaches evolve, and the person closest to the problem often makes the call., * Design, build, and maintain AI-driven features and pipelines that serve enterprise customers at scale * Architect and implement agent-based workflows using LangChain, LangGraph, or equivalent orchestration frameworks * Own systems end-to-end - from experimentation through production deployment and monitoring * Build and improve evaluation pipelines to measure, validate, and iterate on AI system performance * Collaborate closely with the founding team and cross-functional partners - communicating tradeoffs, progress, and technical decisions with clarity * Make pragmatic engineering decisions under ambiguity - ship, learn, iterate * Shape the technical direction of the AI stack as the company scales Our Current Hurdles These are the kinds of problems you'll walk into on day one: * Intelligent retrieval across heterogeneous approaches - our agents need the right information at exactly the right moment. The challenge isn't picking one retrieval method; it's combining RAG, graph-based retrieval, and other approaches into a unified strategy that fetches the most relevant content precisely when the agent needs it - no more, no less. * Agentic workflows that solve real-world problems - it's building workflows robust enough to handle the unexpected. When an agent hits an edge case, missing data, or a situation it wasn't explicitly designed for, it needs to reason through it - leveraging available context, escalating to a human when it can't, and never silently failing. * Evaluation beyond vibes - we need systematic, reproducible evals that actually predict real-world performance. If you've built custom evaluators for RAG or agent workflows, we want to talk. * Execution and real-world integration - an agent that only surfaces insights isn't enough. We're building systems where agents take action - integrating with external platforms, executing workflows, and doing real work with the information they have, combined with human-in-the-loop checkpoints that keep enterprise trust intact. ## Related Videos - [API Design - Getting Started](https://www.wearedevelopers.com/videos/33-api-design-getting-started) - [Developing the Backend with Stefan Lingler, CTO at Shpock](https://www.wearedevelopers.com/videos/100360-developing-the-backend-with-stefan-lingler-cto-at-shpock) - [Your imaginations is (no longer) the limit: how Generative AI empowers people to be creative](https://www.wearedevelopers.com/videos/741-your-imaginations-is-no-longer-the-limit-how-generative-ai-empowers-people-to-be-creative) - [Building AI Applications with LangChain and Node.js](https://www.wearedevelopers.com/videos/1512-building-ai-applications-with-langchain-and-node-js) - [Rest API Antipatterns](https://www.wearedevelopers.com/videos/100208-rest-api-antipatterns) - [Nest.js - TypeScript in the backend can also be clean](https://www.wearedevelopers.com/videos/1033-nest-js-typescript-in-the-backend-can-also-be-clean) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [Navigating the AI Shift](https://www.wearedevelopers.com/magazine/629-navigating-the-ai-shift) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [Graph and AI Trends 2026: Why Is AI Running but Not Yet Delivering?](https://www.wearedevelopers.com/magazine/680-graph-and-ai-trends-2026-why-is-ai-running-but-not-yet-delivering) - [What is Agentic Programming and Why Should Developers Care?](https://www.wearedevelopers.com/magazine/625-what-is-agentic-programming-and-why-should-developers-care) - [Never delegate the understanding](https://www.wearedevelopers.com/magazine/749-never-delegate-the-understanding)