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Job description
Laurel is on a mission to return time. As the leading AI Time platform for professional services firms, we’re transforming how organizations capture, analyze, and optimize their most valuable resource: time. Our proprietary machine learning technology automates work time capture and connects time data to business outcomes, enabling firms to increase profitability, improve client delivery, and make data-driven strategic decisions. We serve many of the world's largest accounting and law firms, including EY, Aprio, Crowell & Moring, and Frost Brown Todd, and process over 1 billion work activities annually that have never been collected and aggregated before Laurel’s AI Time platform.
Our team comprises top talent in AI, product development, and engineering—innovative, humble, and forward-thinking professionals committed to redefining productivity in the knowledge economy. We're building solutions that empower workers to deliver twice the value in half the time, giving people more time to be creative and impactful. If you're passionate about transforming how people work and building a lasting company that explores the essence of time itself, we'd love to meet you.
About the Opportunity
You will own GTM hiring across the revenue org — AEs, CSMs, SDRs, Solutions Engineers, and GTM leadership as we scale. You will work closely with our VPs of Sales and CRO to build a rigorous, high-taste hiring process that produces a revenue team capable of executing our revenue targets this year and beyond.
This is a build role. You are joining a talent function being built from scratch. You will help define what great GTM hiring looks like at Laurel and own it.
What You Will Own
Full-cycle GTM recruiting: AEs, CSMs, SDRs, Solutions Engineers, and sales leadership
10–15 active requisitions at any time; 6–10 hires per quarter as baseline
Outbound sourcing — you know where great enterprise SaaS sellers come from and you go get them
Building relationships with top-performing GTM talent before they are looking
Hiring manager partnerships with Sales and CS leadership: intake rigour, structured assessment, calibration
Closing candidates: equity story, growth path, mission — making the case compellingly
Multi-geography hiring across New York, San Francisco, London, and Los Angeles
What We Are Looking For
Must-have
5+ years full-cycle GTM recruiting at a high-growth enterprise SaaS company
Deep knowledge of what a great enterprise AE looks like — you have hired them, assessed them, and been wrong about them in the past
Experience hiring across the GTM stack: AEs, CSMs, Solutions Engineers, and sales leadership
Strong outbound instincts and a genuine talent network in enterprise SaaS
Structured interviewer and coach — you run calibration sessions, not just debrief calls
Track record closing candidates who had competitive offers
Will set you apart
You have recruited for companies selling into law firms, financial services, or high growth SAAS companies
You understand the difference between an SDR-to-AE motion and an enterprise direct and partner first motion
Strong opinions about comp structures, equity messaging, and closing against public-company offers
Experience hiring GTM talent across multiple geographies simultaneously
You have rebuilt or improved a broken GTM hiring process, not just filled reqs
How We Work
AI-first. We automate outreach personalisation and pipeline review before asking for headcount to cover them.
Unreasonable Hospitality. The candidate experience is employer brand. We design moments that make finalists feel genuinely seen — not just evaluated.
Data-driven. Pipeline health, source quality, time-to-offer, offer acceptance rate. Decisions made with data.
Tour of Duty. Explicit about what this role can become. We invest in your growth and expect you to invest in ours.
What Laurel is
Laurel is building the system of record for human and agentic work.
The trust Laurel has built with regulated enterprises—and, even more importantly, their end-users— has enabled it to be the first company ever to deploy a multimodal desktop agent at scale (45K+ professionals and 70+ countries). This enables Laurel to collect work that no one else has.
Half of enterprise work happens in systems with no API or OS-level access which no tech—other than a multimodal desktop agent—can capture.
Laurel collects more than just metadata; it captures the work itself. And to make the data useful, Laurel builds workflows that requires the professional who did the work to label it accurately, creating a reinforcement learning flywheel that nobody else in the world has.
The result is +2B (and counting) of human-verified labels, creating what is becoming the world’s largest repository of last-mile enterprise data and workflows.
How Laurel did it
Laurel developed its data infrastructure by creating the AI Billing category in professional services (legal, accounting, consulting). Laurel earned the trust of the enterprise (in the form of +$1B in profit returned) and end-users (in the form of 1.53M work hours returned) to collect all digital work, even work happening in digital systems that were previously invisible to AI.
Laurel is now re-running the same playbook in financial services (insurance, banks, private equity, etc). Same data infrastructure. But instead of billing, it is creating the Work Observability category to expand its feedback loop.
What’s next
Packaging its last-mile data in the form of
Post-training: an open-source model on Laurel’s data improved performance by 33%, matching the frontier, at 1% of the cost.
Context: Feeding this data into a frontier model’s context window improves task automation by 21%.
This is how Laurel accelerates its growth from 4x YoY (current) to +10x YoY).