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Engagement Manager

ReadyOn

Company Overview

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.

Engagement Manager Role

Reporting initially to the Vice President of Enterprise Partnerships (and partnering closely with the CEO), the Engagement Manager will own enterprise deployments end-to-end, from signed SOW through implementation, change management, value realization, and early expansion. As a forward-deployed, high-touch partner, you will bridge technical capability and client operational reality, weaving our solution into their daily workflow.

You will work directly with Fortune 500 customers to translate complex labor operations into clear, outcomes-driven deployment plans. Beyond delivering successful, referenceable rollouts, you will play a foundational role in ReadyOn's growth by codifying your execution into reusable playbooks, dashboards, and templates that form our Enterprise Delivery Playbook.

Ideal Candidates
  • Thrive in the Last Mile: You are an operator who is comfortable on the floor with clients as much as you are in the boardroom. You find energy in the ambiguity of early-stage deployments and prefer building solutions over managing status updates.
  • Translate Complexity: You have an exceptional ability to simplify. You can explain how an AI model or a complex data integration works to a non-technical frontline manager in a way that makes them feel empowered, not confused.
  • Own the Outcome: You take complete ownership of the client's success. You don't wait for product requirements to be handed to you; you identify the gap, build the workaround, and then go to the Product team to advocate for the permanent fix.
  • Build for Scale: You have a systems-thinking mindset. While you are happy to do the bespoke, manual work required to win a specific customer, your primary goal is to ensure you never have to do that same task manually for the next 10 customers.
  • Partner, Not Vendor: You view ReadyOn as a strategic extension of the client's business. You instinctively look for ways to expand our footprint not by selling, but by delivering so much value that expansion is the natural, logical next step.
Responsibilities & Impact
  • Own End-to-End Enterprise Deployments: Serve as the single-threaded owner for complex enterprise rollouts - from SOW signature through go-live. Orchestrate internal workstreams across Product, Engineering, AI, and Operations to execute deployment plans, lead technical/business alignment, and maintain delivery momentum in ambiguous environments.
  • Drive Executive Alignment & Value Realization: Act as a trusted advisor to enterprise stakeholders. Move beyond standard relationship management by defining concrete success metrics (labor savings, efficiency, ROI), engineering deployment plans backward from those outcomes, and leading QBRs that reinforce measurable impact and set up long-term confidence.
  • Enable Rapid Product Innovation: Act as the voice of the customer within ReadyOn. Partner with Product and Engineering to translate deployment learnings and enterprise feedback into quantified product insights. Negotiate priorities and trade-offs to ensure our roadmap addresses high-impact customer needs without compromising delivery.
  • Build Scalable Delivery Systems: Turn bespoke deployment work into reusable systems. Codify your execution into repeatable implementation playbooks, success frameworks, and customer health dashboards, establishing the foundational Enterprise Delivery Playbook that allows our team to grow with consistency.
  • Enable Enterprise Expansion: Shape expansion pathways (new sites, regions, or lines of business) based on delivery outcomes. Partner with Sales on renewals and upsells using credible impact data to position ReadyOn as a long-term strategic partner via a crawl-walk-run deployment strategy.
Your Background
  • 4-10+ years of experience in high-growth enterprise software environments, professional services, or management consulting.
  • Technical Implementation Leadership: Proven track record of leading complex, multi-stakeholder enterprise software deployments, specifically in AI-native or high-data environments.
  • Technical Fluency: Deep understanding of integrations, APIs, data flows, and system dependencies. You are comfortable navigating technical complexity and architectural trade-offs in partnership with Engineering teams, even without needing to write production code yourself.
  • Team Development: Experience mentoring and developing team members around you.
  • Cross-Functional Orchestrator: Demonstrated success in forward-deployed or professional services roles where you successfully bridged the gap between product/engineering teams and non-technical client operators.
  • Domain Advantage: Strong interest or previous experience in labor operations or workforce management is a significant advantage
Vacancy posted 2 days ago
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