AI Engineer, Developer Ecosystem

StackOne • Full-time • San Francisco, CA, US • 11h ago

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.