ReadyOn→
Deployment Strategist at ReadyOn in San Francisco
Job Description
Deployment Strategist
Location: San Francisco
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.
Deployment Strategist Role
Joining the Enterprise Partnerships team, the Deployment Strategist works across multiple customer deployments and alongside our Engagement Managers to turn complex labor environments into working ReadyOn deployments. You will contribute directly to live Fortune 500 implementations: building configurations, running data analyses, supporting training delivery, and joining customer engagements.
You will also build the structured product intelligence that helps Engineering and Product understand what is happening in the field. The best people in this role become the team’s institutional memory, and the role grows from deployment execution into product synthesis, analytical infrastructure, and solutions work. Strong performers move into Engagement Management, Solutions Engineering, or Product roles.
Ideal Candidates:
Learn by Doing: You get up to speed quickly in unfamiliar tools, data, and operating environments, and you are comfortable contributing to live Fortune 500 deployments from day one.
Turn Data into Decisions: You break down ambiguous operational questions, work directly with messy data, and move quickly from analysis to a clear recommendation.
Own Your Workstreams: You take ownership of multiple workstreams at once, work out what needs to happen next, and follow through without close supervision.
Build for Reuse: You look for the template, script, or playbook that makes the next deployment faster than the last, and you build it.
Be the Bridge: You explain technical and analytical findings clearly to engineers, product managers, and non-technical operators, and you turn what you see in the field into structured product feedback.
Responsibilities & Impact
Execute Client Deployments: Own defined deployment workstreams, including go-live preparation, configuration troubleshooting, and manager training. Lead relevant client calls and bring technical and analytical depth to customer conversations.
Design Configurations and Solutions: Translate employer data and rules into system-ready configurations. Own solution design details in pilots and early-stage deployments, and contribute to validation and UAT testing.
Turn Field Insight into Product Direction: Surface field-level product gaps to Product and Engineering, grounded in deployment data. Write feature requests informed by cross-customer pattern analysis.
Deliver Data Analysis and Reporting: Build deployment health dashboards, cross-customer operational analyses, and adoption and usage metrics. Run ad hoc data pulls for customer-facing deliverables such as board decks and QBR materials.
Automate and Reuse: Build reusable templates, scripts, playbooks, and analytical frameworks that make each subsequent deployment faster, and support the data warehouse and internal analytics infrastructure as it develops.
Your Background
0-3 years of professional experience, with a degree in Computer Science, Economics, Business, Data Analytics, Mathematics, Engineering, or a similar analytical field.
Analytical Problem-Solving and Data Fluency: You structure ambiguous operational questions into analyses, work comfortably with raw data (exports, API responses, unfamiliar schemas), and spot patterns across data sets and engagements.
Technical Orientation: You pick up new tools and schemas quickly, think in systems, and have basic comfort with code: you can read scripts, automate repetitive tasks, and debug configurations. You know enough about APIs, data sync, and webhooks to diagnose problems and talk with engineers.
Execution and Operational Rigor: You manage several concurrent workstreams, follow through without close supervision, and build repeatable processes that make the team more consistent.
Internal and External Communication: You distill complex information clearly on client calls and write summaries, feature requests, and status updates that stand on their own. You work well across engineering, product, and customer teams.
AI-assisted development tools: Claude Code, Cursor, nice to have: Python or BI tool experience
Nice to Have: Exposure to enterprise SaaS, workforce management, or labor operations.