AI Engineer, Developer Ecosystem

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 25,000 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.
🚀 We're not hiring a content marketer who can code. We're hiring an AI engineer who loves building in public.
What You'll 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 they're announced. No slide decks without code.

What We're 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 you're proud of
• Technical talks on record
• Community presence
• Builds to learn, not to demo
• Gives direct opinions, backed by data
• Doesn't wait to be unblocked

What We're 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 isn't a "write blog posts and attend conferences" role dressed up as engineering. You'll be
embedded with the product and engineering team
. You'll 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.
You'll have access to a platform that connects agents to any other system safely while optimising token usage,
and a mandate to show the world what's possible
when those connections actually work well.