About StackOne:

StackOne is the AI Integration Gateway for SaaS products and AI Agents. Backed by GV and Workday Ventures ($24M raised) we help builders of SaaS platforms and AI Agents orchestrate hundreds of scalable accurate and enterprise-grade integrations. Our platform combines 25000 pre-mapped actions on 200 connectors an AI-powered integration development toolkit plus security by design: a real-time architecture managed authentication and permissions and end-to-end observability.

Join us on our fast trajectory to build the future of agentic integrations.

Were not hiring a content marketer who can code. Were hiring an AI engineer who loves building in public.

What youll actually do

• Build agents and tools in public: demo apps reference implementations MCP servers Claude skills LangGraph workflows. Ship things that are genuinely impressive.

• Own the developer experience: identify friction in our API and SDKs write real feedback back to the eng team and fix it yourself when you can.

• Design and run evals: benchmark tool-calling quality measure agent reliability across integration surfaces build sandboxed test harnesses that reflect production conditions. Publish what you learn.

• Run workshops give talks appear at events: technical sessions on agentic architectures tool-calling patterns context optimization and integration design.

• Publish AI research adjacent to your work: MCP tool schema design context window hygiene eval frameworks for agentic systems RLMF auto-research loops sandbox architecture for safe agent execution.

• Foster community: Discords GitHub demo days office hours. Be the engineer developers trust to give them a real answer.

• Partner with product and engineering: turn new releases into working demos before theyre announced. No slide decks without code.

What were looking for

Hard skills

• Ship production-grade agents

• Deep MCP / tool-calling fluency

• Built plugins skills extensions or agents for real usage

• Designs evals and benchmarks for agentic systems

• Builds sandboxes for safe agent testing

• Understands context optimization

• Reads AI research papers and applies them

• TypeScript and/or Python at minimum

Soft signals

• GitHub history youre proud of

• Technical talks on record

• Community presence

• Builds to learn not to demo

• Gives direct opinions backed by data

• Doesnt wait to be unblocked

What were not looking for

• Someone who needs to ask permission to write a blog post or be taught on how to open a PR

• Someone whose agent experience is only a weekend hackathon project

• A conference talk collector with nothing on GitHub

Topics you should have opinions on

MCP A2A protocol tool-calling schemas context window optimization evals & benchmarking agent sandboxes LangGraph / DSPy RLMF / RLM harnesses auto-research loops code mode / long-horizon agents RAG vs. tool-use tradeoffs enterprise auth for agents multi-agent orchestration prompt caching strategies AI safety boundaries sandbox isolation patterns LLM leaderboard literacy

This is a real engineering role

This isnt a write blog posts and attend conferences role dressed up as engineering. Youll be embedded with the product and engineering team. Youll ship code that ends up in our SDKs our docs and our sample repos.

The AI agent ecosystem is moving fast enough that the line between DevRel and R&D is blurring. We want someone comfortable sitting in that blur writing a technical post about eval design for tool-calling reliability because they spent two weeks deep in it building a sandbox harness to reproduce a flaky agent behavior not because someone briefed them on a slide.

Youll have access to a platform that connects agents to any other system safely while optimising token usage and a mandate to show the world whats possible when those connections actually work well.

Required Experience:

IC