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Applied AI Engineer: Build Agentic Tools & Workflows

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Arcade AI, Inc
Full Time position
Listed on 2026-06-15
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer
Job Description & How to Apply Below

Applied AI Engineer

Everyone's talking about AI. But here's the truth:
ChatGPT can't send your emails. It can't book your flights. It can't even order you lunch.

Why? Because AI is trapped in a chat box. It can't take real actions in the real world.

We are changing that forever. We're not just building another AI company - we're creating the infrastructure that will power every AI application you'll use in the future.

The Revolution Needs You

Every AI app needs agentic "tools" - special functions that let AI models take real actions. Without tools, AI can only chat. With tools, AI can actually do things. We're building the actions runtime that allows AI agents to safely take real‑world actions at enterprise scale. As an Applied AI Engineer on the Tools team, you'll push the boundary of what "a tool" even means at Arcade — designing agentic tools that go beyond deterministic API wrappers, building agents that build new tools, and composing tools into workflows that solve higher‑level problems.

Why

This Is The Opportunity of a Lifetime
  • 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 Lang Chain and Llama Index. 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 :
    We're at the inflection point of AI adoption. The biggest problem isn't better models - it's connecting AI to real‑world actions. 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 investors have backed Databricks, Clickhouse, Mongo

    DB, Perplexity, Cohere, Scale

    AI, 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. The next leap in agent quality lives inside this team's work, and you’ll be the applied‑AI seat that pushes it forward.

Three real problems define the role. Agentic tools vs.

deterministic tools.

Most tools today are deterministic: call X API with Y arguments, get Z result. That model breaks down for entire classes of agent work — research a topic, summarize a thread, decide which of three accounts to act on. Agentic tools, the ones that internally reason, plan, or call models are the answer, but the design space is wide open. When is agentic better than deterministic?

How do you make an agentic tool fast, reliable, and debuggable? You’ll set the bar for what these look like at Arcade.

Agents that build tools.

The toolkit catalog is too big for hand‑crafting to scale. We need agent harnesses that can take a vendor's API and produce a high‑quality toolkit — design, code, eval, docs with a human in the loop only where the human is actually needed. There's early work on this already. You’ll take it from a prototype into the production pipeline that produces the next thousand tools.

Workflows

that compose tools.

Individual tools solve narrow problems. Real customer outcomes: "close the quarter," "triage the inbox," "stand up the integration" need many tools, chained, with the right control flow. We need to figure out what the right primitive looks like above the tool layer, and you’ll lead that design.

The most honest thing we can say about this work: most of the problems you'll be solving didn't exist three months ago. There's no prior art. There's no known solution. If that's the part of the job that makes you nervous, this isn't the right role. If that's the part that makes you lean in, it is.

We do real experiments. We form hypotheses. We publish learnings. Research is part of…

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