ReadyOn.AI is an AI-native Labor Operating System that is redefining how the world’s largest enterprises manage frontline labor. Born out of a Stanford AI Lab, the company applies advanced AI and market-design principles to one of the hardest optimization problems on earth: matching the world’s 2.7 billion frontline workers to the right shifts, in real time.

Frontline workers now expect the same flexibility and autonomy that gig platforms provide, while large employers face relentless pressure to meet aggressive labor-cost targets. ReadyOn bridges that divide with a system of action that predicts workforce demand, dynamically matches it to an employer’s supply of employees, and automates the thousands of staffing decisions made daily across complex, multi-site operations.

The platform is already proven at global scale, powering labor operations for several of the world’s largest enterprises. Landmark customers include a F250 food-service enterprise (300K employees across 16 countries; $7B+ annual labor spend), a F500 hotel group (250K+ employees; $5B+ annual labor spend), a F250 entertainment operator (75K employees; $4B+ labor spend). Across these deployments, ReadyOn has proven that scheduling was never the real problem. It was a symptom. The true challenge is how to match people and work dynamically at scale. ReadyOn solves this problem with an AI system of action that transforms labor from a fixed cost into a strategic advantage, reshaping how enterprises think about workforce design altogether.

Headquartered in San Francisco with 80 employees, ReadyOn grew 8x year-over-year revenue growth in 2025, driven by multiple seven-figure Fortune 250 enterprise deployments and a rapidly expanding pipeline.

Transform How Frontline Work Runs

Enterprises struggle to manage hundreds of millions of dollars in frontline labor spend due to decades-old software and manual processes, creating massive, avoidable costs. Frontline labor often represents 40% of the P&L, yet the systems managing this $3 trillion market were built for static schedules and limited flexibility.

ReadyOn was founded to reject that paradigm. Staffing is not a scheduling problem; it is a real-time supply–demand orchestration problem. ReadyOn is an AI-native labor operating system, built from the ground up for AI agents to perform real-time labor optimization, much like ridesharing platforms that match drivers and riders in real time, but applied to frontline labor instead of fixed, one-size-fits-all schedules.

Who’s Building It

AI is not a bolt-on feature in our platform. Every decision, from demand forecasting to shift assignment, flows through an adaptive, autonomous decision layer that learns from operational data and continuously optimizes for cost, compliance, and worker satisfaction. Behind that system is a founding team of experts in labor markets, enterprise software, and AI-enabled platforms:

Reza : Engineering leader who scaled enterprise systems at Google, Yahoo, and AT&T

Dominic : Operator who optimized labor-intensive operations in 21 countries

Mohammad : Stanford professor and leading expert in algorithmic market design

ReadyOn has already proven product–market fit with multiple multi-million-dollar customers, consistent expansion within existing accounts, and measurable ROI that moves stock prices.

GTM Engineering Lead Role

Reporting to the Head of Marketing, the GTM Engineering Lead will build and run the automated engine that turns account signals into first meetings with the world’s largest frontline employers. You will own the systems behind ReadyOn’s outbound and inbound motion: the signals that tell us an account is ready, the enrichment and messaging that reach the right people, and the handoff that puts a qualified meeting on a seller’s calendar.

You will work hand in hand with Marketing every day, and closely with Sales, RevOps and Security, with HubSpot as the system of record and AI at every step. Beyond the meetings themselves, you will play a foundational role in ReadyOn’s growth by codifying every play into the runbooks, templates and prompts that let the team scale what works.

Ideal Candidates

Builder First: You would rather ship a working play this week than plan a perfect one next quarter. You find energy in turning a rough brief and a raw account list into a live sequence.

Signal Over Noise: You know the best outbound starts with a reason to reach out. You look for real signals, such as a new property, a new general manager or rising labor pressure, and you set the bar for which ones are strong enough to act on.

AI-Native by Default: You use AI every day to research, write and route at scale, and you know where a person still needs to check the work.

Careful With Shared Systems: You move fast without breaking the system of record. You log every change and ask before touching shared data.

Build for Scale: You are happy to do the manual work that proves a play, but your goal is to make sure nobody has to do it by hand again.

Partner to Sales: You measure your work by the meetings sellers want to take. You care about the handoff as much as the send.

Responsibilities & Impact

Build the Signal-to-Meeting Engine: Stand up standing feeds for the signals that matter to frontline employers, such as new facilities, new general managers, labor pressure, incumbent systems, bargaining activity, earnings calls and trade press. Score them, and route only the strongest into plays that run end to end, from a target account to a live sequence with no manual steps.

Own Enrichment and Messaging at Scale: Run the account, contact and persona tables in Clay, and generate per-person messages from Marketing’s core message with Claude and Clay.

Run Campaigns at a Steady Cadence: Launch one-touch, multi-touch and nurture campaigns across email and LinkedIn in tools such as Apollo, HeyReach and HubSpot, and build the webinar and inbound plays: site tracking, form routing, and the attendee, no-show and demo sign-up sequences.

Keep Every Send Clean: Own deliverability (sending domains, mailboxes, warmup and verification) and the automated send screen, so nothing goes to the wrong person.

Own the Handoff: Build the routing, suppression and handoff workflows that get every positive reply to the right owner the same day with a task and an alert, and take contacts out of sequences once they reply or book.

Keep HubSpot Trustworthy: Manage campaign lists, tags and fields under a shared naming rule, keep the data your plays bring in clean, and keep deal fields current through automation. Partner with RevOps on every field, workflow and sync change, and step in on administration and reporting when RevOps needs a hand.

Learn What Works: Read results at the campaign level in HubSpot, with Sales, to decide which plays and signals to scale and which to retire.

Build Scalable Systems: Automate the resea