> Markdown version of [/jobs/ext/1174262-talent-systems-engineer](https://www.wearedevelopers.com/jobs/ext/1174262-talent-systems-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Talent Systems Engineer - **Company:** AGENTIO INC. - **Location:** New York, NY, United States - **Experience:** Experienced - **Salary:** $100,000.0 - $180,000.0 - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Systems Engineering, Backup Devices - **Published:** July 3, 2026 - **Apply:** https://www.indeed.com/viewjob?jk=70707297a99245f8 ## About the Role The right person for this role has a genuine builder mentality, and has already proven they can ship. We care far more about what you have built and who used it than where you have worked or how long you have been doing it. The one thing we will ask every candidate: tell us about an automation or system you built that was adopted by people other than yourself. What problem did it solve, how did you build it, and what was the impact? We will want specifics. Beyond that: * You see a manual process and your instinct is to fix it. * You are data literate. You can pull a report, build a dashboard, think in funnels, and know what the numbers are actually telling you. * You pick up new tools fast and have opinions about which ones are worth learning. * You are energized by the idea of applying systems thinking to the problem of finding and attracting exceptional people. * You are comfortable in ambiguity and motivated by building something that does not exist yet. You do not need to know our current stack coming in. We will teach you what we are using today. What we cannot teach is the instinct to build. 2+ years of experience in ops, GTM, RevOps, recruiting, or a similar role - what matters more than the years is what you have shipped. ## Description Agentio is an intelligence company. Our product is built on a compounding data moat - every campaign makes our models smarter, which drives better outcomes for brands and creators. Our talent function should work exactly the same way. Most recruiting functions are sitting on a goldmine of signal they have never connected. Candidate behavior, interview performance, sourcing conversion, outreach response rates - it is all there, spread across a stack of tools that do not talk to each other. The intelligence layer on top of it does not exist yet. We are building it. We need someone to design and own that layer - the systems, automations, and agent orchestration that power every strategic talent decision we make, and that give our recruiters back the time to do what they do best: build relationships and close exceptional people. Every hire should make the next hire smarter. That is the compounding moat we are after, and this role is how we build it. This role is a natural fit for someone with an interest in product or ops. The work itself is genuine systems and infrastructure building, and the skills compound in that direction. What you'll build * The intelligence engine that powers strategic talent decisions - identifying which candidates we should be going after, what we know about them, and how to prioritize our energy before we have even made contact. * An orchestration layer that automates the repeatable and surfaces the important, so our recruiters spend their time on relationships, not admin. * AI agents that map, identify, and surface exceptional candidates before we know we need them, coordinated to work with one another across the full talent lifecycle. * The connective tissue across our talent stack so that signal compounds across tools rather than sitting in silos. * A warm intro engine - mapping Agentio's network against target candidates and making it frictionless for founders, investors, and teammates to open doors we could not knock on ourselves. * Pipeline analytics and dashboards that give the recruiting team real visibility into what is working at every funnel stage. * Feedback loops that connect post-hire performance data back into sourcing criteria, so the system gets smarter over time. Our stack today - and where it's going Right now we are working across Gem, Ashby, Clay, and Metaview. The landscape is moving fast enough that what is in the stack today may look completely different in 18 months, and that is part of what makes this interesting. We want someone who develops opinions about what we should be building on, what we should walk away from, and what we should build ourselves. ## Related Videos - [Hiring AI Native Talents](https://www.wearedevelopers.com/videos/100268-hiring-ai-native-talents) - [Edit Your Future: Queerverse Radical AI](https://www.wearedevelopers.com/videos/909-edit-your-future-queerverse-radical-ai) - [Model Based Systems Engineering in an Agile Product Development Process](https://www.wearedevelopers.com/videos/68-model-based-systems-engineering-in-an-agile-product-development-process) - [Hate organising your photos? 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