Luna is a new startup inside monday.com Agent Labs, founded and run by monday.com's CEO, the program that spins up independent startups from scratch. Each one runs like a seed-stage company: a tiny team, a real product, real users, with monday.com's backing and distribution.
Luna already has a live product, a pile of open problems nobody has answered yet, and no layers between you and the person making the calls. We're hiring the engineer who builds it. You work directly with the founder, monday.com's CEO. Decisions happen in the conversation, not in a roadmap review. You own the engineering. The founder owns the product.
Meet Luna, the first generation of autonomous agents, which proved that agents can do real work. It also proved they broke. Unbounded self-coding that can touch any key. Everything is riding on raw LLM reasoning, so execution drifts. No way to see what actually happened.
Luna is the second generation. It still learns and improves itself, but on a scaffold built for trust:
Deterministic workflows, the agent writes and refines, then runs on a real engine instead of re-reasoning every time.
A one-way vault. Secrets resolve below the plugin layer and never enter the model's context.
Rollback on every change, an audit trail on every action, and approval gates on anything sensitive.
Everything else is a plugin: tools, channels, skills, isolated per agent.
Luna isn't a chatbot. You describe the goal, it builds the workflow, you correct it, and it runs reliably next time. A private Luna spins up in about a minute today.
There's no playbook yet for what a good agent is in this context. We're writing it with real use cases and real constraints. That's the job.
An experienced builder at heart. You'd rather ship the thing than pitch it. This is an engineering role, not a founder track.
Instincts about agents. Before writing a line, you ask what the model must never be allowed to touch, how you'd know it failed, and what happens when it does. Shipping ten agents isn't required. Having thought hard about one is.
Independent. Hand you something vague, and you come back with something that works.
Strong fundamentals in distributed systems and reliability, with production experience.
Nice-to-have: LLM tooling in production, tool use, sandboxing, and evals. A security-minded way of building.
Lead quality assurance for agent-driven platforms and AI models, focusing on validating intelligent agents and ensuring the reliability and scalability of AI systems.
Architect and demonstrate AI/ML solutions for customer technical teams, lead proof-of-concept engagements, support adoption across APJ, and influence product roadmaps through customer feedback.
Design and implement AI agents, automation workflows, and data solutions that streamline investment and corporate processes, integrating existing systems while improving data quality, reliability, and adoption.
Build and scale production AI solutions that improve business workflows, partnering with users to integrate systems, deliver reliable AI features, and drive measurable gains in speed, quality, and adoption.
Build and scale reusable AI platform capabilities, backend services, APIs, and full-stack product features, connecting applied generative AI to modern web experiences while improving reliability and performance.
