AI Engineer

About this role

Everyone's building AI agents, but almost nobody gets them to production.

Building an impressive demo is easy. Building an AI agent that can securely take action inside enterprise systems is hard. The moment an agent accesses customer data, executes a workflow, or makes changes on behalf of a user, authorization, governance, and trust become the real engineering challenge.

Arcade is the MCP runtime that gives agents the power to do both seamlessly. We connect agents to the systems they act in, then give each one a permission slip and a paper trail - proof of what it's allowed to do, and a record of what it did. That's what makes AI safe to turn loose: real actions, on real systems, already shipping inside Fortune 100 companies.

The Revolution Needs You

Every AI app needs agentic tools that let AI models take real actions. Without tools, AI can only chat. With tools, AI can actually do things. We're building the definitive tools catalog, actions platform, and governance model that will unlock AI's true potential. Think Zapier for AI Actions. Think Auth0 for AI. Think really big.

Why This Is The Opportunity of a Lifetime

- Founder-Market Fit : Our CEO previously founded Stormpath (acquired by Okta), where he Traction: Real deployments with Fortune-100 customers like Morgan Stanley and Open Table

- Founder-Market Fit: Our CEO previously founded Stormpath (acquired by Okta), where he created the first Authentication API for developers. He's done this before - and this time the market is 10x bigger. Our CTO led the vector database team at Redis, shipped 100+ LLM applications, and is a contributor to LangChain and LlamaIndex. He knows this space better than anyone.

- Dream Team: We've assembled authentication, integrations, distributed systems, and AI experts from Okta, Redis, Microsoft, Splunk, Ngrok, Google, Airbyte, Disney, and HPE who've built and founded multiple successful developer platforms.

- Perfect Timing: Every enterprise is racing to put agents in production - almost none get there. The problem isn't better models, it's proving which agent can take which action, on behalf of which user, against which system. That's us.

- Massive Market : We're building critical infrastructure for the biggest technological shift of our generation. Every AI app will need what we're building.

- Backed By The Best: Our Series A round is led by SYN Ventures, with strategic investment from Morgan Stanley and Wipro. Our earlier investors have also backed Databricks, Clickhouse, MongoDB, Perplexity, Cohere, ScaleAI, Confluent, Elastic, and Firebase. They see what we see - this is going to be huge.

The Challenge

You'll report to the Engineering Manager for Abilities, Builder, and Growth. Abilities exists to enable agents to get things done - full stop. We take a job to be done that someone's trying to do (or that we want to enable) and figure out how to deliver it, whatever that takes: updating the Arcade Engine itself (Go), building an MCP server, standing up an entirely new service to support Skills, building an agent and figuring out how to ship it via MCP, or whatever's next in this space.

You're building the tools, skills, and platform underneath them - the pieces that let an agent do things nobody's figured out how to do yet. Every time an agent hits a wall in Cowork or ChatGPT - some action it just can't take - that's a rough edge the Abilities Team gets to fix, at the scale of every customer who hits it.

The team is early and the surface area is growing fast, so you'll have real ownership from day one, including a hand in the patterns every toolkit author after you inherits, and in agents-as-tools (sub-agents), which Abilities also owns.

What You'll Do

- Build the systems that build systems - dedicated agent harnesses that write and maintain tools themselves, plus agents-as-tools (sub-agents) that let one agent call another as a capability

- Take a job to be done and own delivering it - whether that's changes to the Arcade Engine, a new MCP, a new service, or something nobody's built yet.

- Build new toolkits and abilities - turn a vendor's API (or an internal need) into tools, skills, or agent abilities that get customers a real outcome, not just a demo.

- Fix the rough edges - when an agent hits a wall, figure out what's missing and build it, at the scale of every customer who'll hit that same wall.

- Maintain and improve the existing catalog - fix breakages, sharpen tool descriptions and response shapes, keep quality high as upstream APIs change.

- Build the next generation of evaluation and testing for abilities - go beyond pass/fail checks to know, programmatically, what a tool is actually doing across agents, models, and surfaces.

- Engage with and push the MCP ecosystem, where the standards for agent tools are still taking shape.

Required Skills

- 4+ years of software engineering experience shipping production code, with a track record of owning projects end to end.

- Strong Python and/or TypeScript

- Experience building and consuming APIs at scale - REST, auth flows, pagination, rate limits, and the messy parts of real-world integrations.

- A real desire to build the parts of this stack that don't exist yet - comfortable when the answer isn't known and you have to figure it out.

- LLM application experience - prompting, retrieval, tool use, or agent design.

- A testing mindset - tests and evals that catch real failures, not just green checkmarks.

- Comfort with ambiguity - early team, a narrow charter that will expand, decisions made with incomplete data.

- Stop-at-nothing energy to get the job done.

Bonus Points

- Familiarity with the MCP (Model Context Protocol) ecosystem or similar agent-tool protocols - extra bonus if you've filed an issue against the spec.

- Familiarity with Go

- You've built evals or measurement systems for ML/AI behavior.

- Prior experience at an API platform, data-warehouse, integra

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