How Masai School Recovered After Its Pay-After-Placement Model Was Tested
- yanabijoor
- 4 hours ago
- 3 min read
The problem
In India, millions of college students graduate every year, but its labor market continues to face a shortage of job-ready digital talent. Many software engineering, data analytics, and emerging AI roles are unfilled, while graduates remain underemployed. Traditional higher education is still heavily weighted toward textbook learning, rather than teaching industry-relevant skills.
For non-elite students, particularly those in smaller towns, this gap has serious consequences. Without access to practical training, networks, or employers willing to take risks on inexperienced candidates, many capable college graduates are locked out of the fast-growing digital economy and its income potential.
The solution
To address this mismatch, Masai School was built around a single premise: your education should directly lead to employment. Masai provides intensive, full-time training in software development, data analytics, and AI, designed to mirror real engineering environments through hands-on projects, collaborative projects, and continuous assessment.
The company initially scaled through a pay-after-placement model using income share agreements (ISAs), allowing students to enroll with little or no upfront money down and repay only after getting a job. Alongside training, Masai worked directly with employers to place graduates into junior technical roles, tightly connecting learning outcomes with market demand.
Why is this innovative?
Masai’s core innovation lies in how it structures incentives and risk. Unlike conventional education providers, which are paid regardless of students getting a job. Masai’s revenue has historically depended on whether students actually secure and keep employment. If placements fail, Masai bears the cost.
This approach reframes education as a human capital investment rather than a retail education product. For Masai, the quality of education is not a marketing promise but a financial necessity. However, this model was tested during a hiring freeze and layoffs in India's tech industry.
Business Model
The collapse of startup hiring between 2022 and 2024 revealed the limits of a single-revenue approach. Masai restructured its model to improve cash flow and scalability. Today, it operates as a for-profit education company with three revenue sources:
Income share agreements and tiered pay-after-placement fees, recalibrated so repayments scale with a graduate’s actual salary, preserving fairness while protecting Masai’s unit economics.
Upfront tuition through prepaid programs, including courses delivered in partnership with the Indian Institute of Technology (IIT) and the Indian Institute of Management (IIM), is designed for working professionals, college students, and learners outside the placement funnel.
Employer-linked offerings, including AI-focused upskilling programs and an AI-powered job-matching platform that connects vetted talent, including non-Masai candidates, to hiring companies.
Adding additional revenue streams reduces Masai’s exposure to hiring cycles while retaining its outcome-driven DNA.
Funding and credibility
Masai has raised approximately $14.7 million (USD) across multiple funding rounds, backing its transition from a pure ISA model to a more balanced, multi-product education platform. While revenue declined during the peak of the hiring slowdown, the restructuring paid off.
By FY25, Masai reported revenue close to $12 million USD, cut losses by nearly 70% year-on-year, and claimed EBITDA profitability beginning January 2025. The company now projects $25 million USD in revenue for FY26, with an expected net profit of around $4.2 million USD, signalling a shift from survival to sustainability.
What is the impact?
Masai has trained over 40,000 learners and placed more than 10,000 students into full-time roles, with an average salary of about $7,300 USD during its peak placement years. The bulk of its learners come from non-elite backgrounds, and the company positions its work as lifting families into stable, middle-income careers rather than providing charitable education.
Beyond placements, Masai now educates tens of thousands of learners through prepaid and AI-focused programmes, with strong uptake from smaller towns. Its expansion into AI-driven personalization and skill-based job matching reflects a broader shift in how technical education and hiring intersect.
What still needs to improve?
Outcome-linked education is complex. Income share agreements require clear regulation and transparency. Placement-driven models risk over-optimizing for short-term hiring needs rather than long-term skills, particularly as AI reshapes software roles.
As Masai scales, it must defend its edge on placement quality, manage rising costs from marketing and institutional partnerships, and prove that profitability can co-exist with accessibility. The next test is whether it can scale without diluting the very outcomes that defined its purpose.
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