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 Tools and Growth. The Tools team owns Arcade's tool catalog — thousands of tools across many services, growing faster than any human can review by hand. These toolkits are what let agents actually do things in the real world, and keeping them broad, reliable, and high-quality is the work that makes Arcade useful.
As a Software Engineer on Tools, you'll build and maintain the toolkits agents depend on: take a vendor's API, design a clean set of tools on top of it, write the integration code, test it against real API behavior, and keep it healthy as those APIs change underneath us. You'll work close to the agent itself — how a model reads a tool description, how it decides which tool to call, and whether the result comes back in a shape the agent can use.
The team is early and the catalog is growing fast, so you'll have real ownership from day one and a direct hand in the patterns every toolkit author after you inherits.
What You'll Do
Build the agents we use internally to build, maintain, and scale our toolkits and skills.
Build new toolkits — take a vendor's API and turn it into a high-quality set of tools that agents can call reliably.
Maintain and improve the existing catalog — fix breakages, sharpen tool descriptions and response shapes, and keep quality high as upstream APIs change.
Write tests and evals that prove a tool does what it claims — against real API behavior and across different models.
Treat LLMs and agents as a first-class part of the job — tune tool descriptions, tool-use behavior, and how an agent actually calls what you built.
Contribute to the shared patterns and tooling that make building the next toolkit faster.
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.
Strong Python and Typescript.
Experience building and consuming APIs — REST, auth flows, pagination, rate limits, and the messy parts of real-world integrations.
A testing mindset — you write tests that catch real failures, not just green checkmarks.
Comfort with ambiguity — early team, a narrow charter that will expand, decisions made with incomplete data.
An insatiable desire to ship.
Bonus Points
LLM application experience — prompting, retrieval, tool use, or agent design.
Familiarity with the MCP (Model Context Protocol) ecosystem or similar agent-tool protocols — extra bonus if you've filed an issue against the spec.
You've built evals or measurement systems for ML/AI behavior.
Prior experience at an API platform, integrations-heavy product, or developer tools company.
Open-source contributions.
Experience at an early-stage startup, and you loved it.
Join The Movement
We're not just building a product - we're leading a movement to transform AI from just chatbots to agents that can take actions against real systems. This is your chance to be at the forefront of that revolution.
If you want to look back in 5 years and say, "I helped build that", then we want to talk to you. Ready to make AI actually useful? Apply Now
Compensation and Benefits
This role offers a competitive salary, equity, and benefits. Compensation is aligned with the range below and determined based on a candidate's background, experience, and performance.