Hire people who already work this way.
Fractional AI-native talent across the full spectrum — engineers, product leaders, automation specialists, data builders, and operators. Every person here is vetted against public proof of shipped AI work: repos, production systems, and measured outcomes. Not résumés. Not slides.
The role spectrum
AI-native is not one job. It's a way of working that shows up across every function. Pick the seat you need filled — or send a brief and we'll match across the spectrum.
AI Engineers
Agent systems, LLM features, and production AI infrastructure.
6 in network view → ~/hire/ai-productAI Product Leaders
Product managers and founders who run AI-native product orgs.
2 in network view → ~/hire/ai-engineering-leadershipEngineering Leaders
Leaders who have rewired real engineering orgs around AI.
5 in network view → ~/hire/ai-ops-automationAutomation & Ops
Workflow automation across n8n, agents, and internal tooling.
4 in network view → ~/hire/ai-dataData & ML
Structured outputs, data pipelines, and eval-driven LLM systems.
3 in network view → ~/hire/ai-marketingMarketing & Content
Growth and content operators with AI-native production systems.
2 in network view →Ways to engage
Hiring here isn't only "ship me X." Sometimes the work is changing a process, installing an agent-first workflow into a flow your team already runs, or mapping where agentic capability would actually facilitate the work. Name the shape in your brief — or let us help you pick.
Ship a build
A scoped artifact: an agent system, an LLM feature, a pipeline, an internal tool. Defined start, defined end, inspectable result.
e.g. Ship an eval harness that gates model changes before release.Install a workflow
Take a flow your team already runs — support triage, QA, research, reporting, candidate screening — and rebuild it agent-first, with humans on the judgment steps.
e.g. Turn manual support QA into an agent loop with human review on edge cases.Change a process
Redesign how a team works — AI-native development practices, spec-driven delivery, review loops — including the adoption work, not just the tooling.
e.g. Move an engineering org to AI-assisted delivery with telemetry to prove it.Map the opportunity
An audit of your flows to find where agentic capabilities actually facilitate work — ranked by leverage, with a build plan. For when you know there's something here but not what.
e.g. Two-week audit of ops and support flows, ending in a ranked automation plan.Embed an operator
A fractional seat — days per week, ongoing — running AI-native execution inside a function: shipping, automating, and upgrading the team's way of working as they go.
e.g. Two days a week running growth experiments on an agent-powered content engine.Not sure which?
Describe the friction in plain words. Picking the engagement shape is part of what the human review does.
start a brief→Do you need this?
A quick diagnostic. If more than two of these read true, a fractional AI-native hire pays for itself in the first engagement.
- There's an AI feature on your roadmap that has slipped two quarters in a row.
- Your team bought the AI tools. Three people use them. Nobody measures anything.
- You have a prototype that demos beautifully and breaks in production.
- A full-time senior AI hire is a 6-month search you don't have time for.
- Manual work is eating a team that should be building.
How it works
Tell us the outcome
Five minutes. What you're trying to ship, your constraints, your timeline. Not a job description — an outcome.
Human-reviewed shortlist
We match against builders with public proof in your exact problem shape. You get two or three names, with the evidence attached, within days.
Start fractional
Scoped build, days-per-week retainer, or embedded sprint. Start small, expand on results, no long-term commitment until trust is earned.
Judge the artifact
Every engagement ends in something inspectable — a repo, a running system, a measured number. The same standard we vet by.
Builders in the network
A sample of the people behind the proof. Every profile links to public showcases of real AI work — the systems they built, not the words they chose.
@brian-scanlan
Engineering lead at Intercom — AI workflows for R&D throughput
view proof → C Claire Vo@claire-vo
Product leader — ChatPRD, LaunchDarkly, and AI product workflows
view proof → D Dexter Horthy@dexter-horthy
HumanLayer founder — team-scale Claude Code and context engineering
view proof → J Jason Liu@jason-liu
Instructor creator — structured outputs, evals, and production LLM workflows
view proof → S Sahil Lavingia@sahil-lavingia
CEO at Gumroad — AI-accelerated product development
view proof → A Anand Chowdhary@anand-chowdhary
Continuous Claude builder — long-running Claude Code PR loops
view proof →Why this network is different
Artifacts over interviews
Nobody gets listed on claims. Public repos, production systems, and measured outcomes are the admission ticket.
Builders, not consultants
Everyone here ships their own work with AI daily. They've been in your seat, and they bring the workflow with them.
Senior talent, right-sized
Get a top-decile AI builder for two days a week instead of a six-month full-time search. Expand only when results show up.
Tell us what you need shipped.
One short brief. Human review. A proof-backed shortlist in days. If we don't have the right person, we say so — no padding the funnel. Sourcing with an agent? It can search the network and file the brief for you over MCP or JSON.