AI Automation Engineer
US$150,000 – US$200,000 / yr base salary
Posted 10 Sep 2026
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 3+ yrs
- Management level
- Individual contributor
- Employment
- On-Site
- Contract
- Full-time
| Base salary | US$150,000 – US$200,000 / yr |
|---|
ABOUT GREENBOARD
At Greenboard, we're building the future of financial compliance. Greenboard is the unified, AI-native compliance operating system for RIAs, fintechs, private funds, hedge funds, and more. It replaces the fragmented mix of legacy tools and automates more than previously possible. By centralizing data and workflows, Greenboard helps firms reduce regulatory risk, simplify their technology stack, modernize how they run compliance, and save money.
Our founding team includes engineers who have scaled products at Amazon, Google, and multiple unicorn startups. We're backed by Y Combinator, General Catalyst, Base10, and other top-tier investors, and have raised over $20M to date. Brand-name financial institutions already rely on Greenboard, and we're growing fast.
ABOUT THE ROLE
You'll be our first dedicated Automation engineer. Our customers are regulated financial institutions, which means a missed bug is not just a bad user experience. It can mean an incomplete books-and-records archive, a marketing review that never routed for approval, or an attestation that quietly failed to send. Quality is part of the product we sell.
You'll own how we test: the framework, the automated suites, and the release process, built from scratch and then embedded with the engineers who ship. You'll also help us solve something most automation engineers have never had to solve, which is how to test AI systems whose output is not deterministic.
There is no existing playbook here. If you like defining the standard rather than enforcing someone else's, this is the seat.
WHAT YOU'LL OWN
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The foundation. Test framework, automated regression suites, and QA practices built from zero, with your call on tooling
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The critical paths. Message archiving and supervision, marketing review routing, employee attestations, and audit trails, which are the flows that cannot break
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Testing the AI. How we evaluate AI-driven detection and review, including golden datasets and quality bars for output that is not deterministic
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Data integrity at scale. End-to-end validation from ingestion of tens of millions of messages a day through to what a compliance officer sees on screen
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The release gate. Bug triage, release sign-off, and the definition of deployment-ready at Greenboard
WHO ARE YOU?
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Love to move fast and build things
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Looking for deep ownership over the quality of everything we ship
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Obsess over the customers you're building for