THE ROLE
The best technical recruiters right now aren't just recruiters. They're builders. AI didn't kill recruiting — it raised the bar for who can do it well.
ThirstySprout is a bootstrapped, profitable technical talent marketplace placing remote engineers — onshore, nearshore, and offshore — with funded tech startups. We compete with Toptal and Turing but with radically different economics: fair margins, multi-year engineer retention, and a demand gen engine already producing consistent deal flow. Over 60% of our current pipeline is AI/ML talent. The rest is top-tier engineering across the full stack.
We need someone who will build our talent supply engine with their own hands. Not someone who advises from the sidelines. Not someone who needs a team of five before they produce results. A builder who recruits — someone who can evaluate whether a candidate's ML experience is production-grade or notebook-level, then go build the pipeline that finds 500 more like them.
This is an agency role, not in-house recruiting. You'll fill positions across multiple clients simultaneously, each with different stacks, cultures, and urgency levels. You'll be client-facing — joining intake calls, presenting shortlists, calibrating on feedback. You need to be comfortable with the pace and accountability that comes with being the product.
The growth path is explicit. Within 6–12 months, you step into leadership with a team underneath you. If you prefer to stay as a high-performing IC, that path exists too — with comp that reflects your output. Either way, you're building from the ground floor.
WHAT YOU OWN
AI/ML & Technical Talent Sourcing
* Build signal-based pipelines that find candidates before they hit the market: who just left a major AI lab, who got promoted, who published a new paper, who engaged with a competitor's post.
* Source across AI/ML roles (ML engineers, AI infra, NLP/LLM, computer vision, MLOps, AI product) and top-tier engineering (full-stack, backend, DevOps, cloud, data). Know the difference and what "good" looks like in each.
* Architect automated sourcing workflows using LinkedIn Recruiter, Clay, Phantom Buster, and custom API integrations. Build a standing inventory of pre-qualified candidates by stack, seniority, and geography.
Technical Screening with Real Depth
* Evaluate candidates with enough technical acumen to earn engineering respect. Read GitHub repos, assess system design thinking, separate genuine ML production experience from tutorial-level work.
* Understand AI/ML hiring nuances: the difference between a research scientist and an ML engineer, what production deployment looks like, why PyTorch skills alone don't mean someone can build a production inference pipeline.
* Build AI-assisted screening: LLM-powered scoring, GitHub parsing, automated candidate briefs.
Client-Facing Delivery
* Join intake calls, present polished shortlists, calibrate on feedback. The candidate brief is your product.
* Manage multiple client pipelines simultaneously with different stacks and quality bars.
Systems & Infrastructure
* Own the ATS architecture and all recruiting data. Automate every stage from source to placement to retention.
* Build dashboards tracking unit economics: cost per qualified candidate, time to fill, submission-to-placement ratio, source channel ROI.
WHO YOU ARE
Non-Negotiables
* You build, not advise. You've used Claude Code, Replit, n8n, Zapier, or raw Python/JS to build recruiting workflows and sourcing automations. You don't tell people what they should do — you show up and do it.
* Real technical acumen, especially in AI/ML. You can evaluate whether a candidate's machine learning experience is production-grade. Engineering teams respect your judgment because you speak their language.
* Agency or marketplace experience. You've filled roles across multiple clients simultaneously with your output directly tied to revenue. In-house-only recruiters will struggle here.
* Systems and unit economics thinker. You can rattle off your conversion rates, cost per qualified candidate, and time-to-fill from your last role. Every manual step is a bug to automate.
* Client-facing and comfortable with it. You can run an intake call, present a shortlist, and calibrate on feedback.
* You want to build with us, not for us. The equity path, the leadership trajectory, the chance to eventually run talent supply as your own business — that's what drives you.
Strong Signals
* You're already building on recruiting APIs or custom sourcing tools. Show us.
* You've placed AI/ML engineers and understand the landscape.
* Background at or competing against Toptal, Turing, Andela, Terminal, Arc.dev.
* Developer or CS background who moved into recruiting. The technical foundation is real.
* Experience sourcing across LATAM, EMEA, or Asia. Spanish/Portuguese a plus.
* 2–6 years in technical recruiting — but what you've built matters more than tenure.
WHAT SUCCESS LOOKS LIKE
First 90 Days
* Full pipeline audit: channels, process, quality, rates. Present findings and automation plan.
* Take over active pipelines. Founder exits day-to-day sourcing within 60 days.
* Demonstrate you can source and screen AI/ML candidates at the quality bar clients expect.
* Ship at least two automations that improve throughput or cut manual effort.
6 Months
* Automated pipelines producing qualified AI/ML and engineering candidates proactively — before reqs come in.
* Throughput up 2x+ with quality holding. Cost per candidate and time-to-fill trending down.
* Client-facing interactions are smooth and trusted. Shortlists are calibrated, feedback loops tight.
12 Months
* Leadership path: managing 1–3 people, training them on your systems, shifting toward strategy and architecture.
* IC path: highest-output recruiter in the company with comp that reflects it.
* Either way: 85%+ 12-month retention. Documented, repeatable system. Talent supply is no longer a founder bottleneck.
COMPENSATION
Base salary competitive for your location. Meaningful per-placement bonuses with no ceiling — your income scales with your output. Equity path earned as you step into leadership. Full autonomy over tools and systems.
This role is for someone who sees the upside in building, not someone optimizing for the highest guaranteed base.
HOW TO APPLY
Don't send a resume. Send proof:
1. Show me something you've built — a sourcing tool, an automation, a workflow, a side project. Loom, GitHub, screenshots — whatever shows it best.
2. Walk me through how you'd evaluate an AI/ML engineer. What signals do you look for? How do you separate production experience from tutorials?
3. Why ThirstySprout, why now?