Data Scientist

Data Scientist

San Francisco

Engineering

In office

Full-time

Ready On 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. Ready On 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, Ready On has proven that scheduling was never the real problem—it was a symptom.

The true challenge is how to match people and work dynamically dyOn 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, Ready On grew 8x year-over-year revenue growth in 2025, driven by multiple seven-figure Fortune 250 enterprise deployments and a rapidly expanding pipeline.

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.

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

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

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

• Data Scientists who thrive in ambiguous, high-impact environments and naturally set technical direction for the Software and Machine Learning Engineers.

• Care deeply about clean scalable machine learning modelling techniques, and are not afraid to rethink default patterns.

• Enjoy working closely with engineering, product, design, and AI research teams to deliver new data-driven experiences customers actually use.

• Focus on best-in-class modelling techniques, not just technical output, and love solving real business problems with data, services, and automation.

• Design, build, and deploy forecasting models that predict key business and customer metrics across workforce planning, revenue, demand, operational, and AI-driven decision-support use cases.

• De…