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About Disha

We believe the future of healthcare is one where everyone carries a personal doctor in their phone—someone who knows them, understands their health journey, and is accessible whenever they need support. Disha is a step toward that future.

Disha is an AI health coach that supports people through personalized, ongoing conversations across chat and voice. We are building it to make healthcare more proactive, continuous, and accessible—not limited to the few minutes someone spends with a doctor.

Disha launched on September 20, 2025. Nine months later, as of June 20, 2026:

• More than 500,000 people used Disha monthly.
• In May alone, users spoke with Disha for over 3.2 million minutes and exchanged more than 6 million chat messages.
• New users spent an average of 17 minutes talking to Disha on their first day.
• We recovered customer-acquisition and AI-inference costs within 90 days and are profitable.

Disha is backed by Elevation Capital and General Catalyst. We are a small team of highly effective people—alumni of the IITs and AIIMS Delhi working alongside experienced healthcare professionals—who take ownership, move quickly, and care deeply about the quality of our work. The founders themselves bring 5+ years of experience in care delivery.

https://disha.health/

https://www.instagram.com/disha.health/?hl=en

The Role

We are looking for an Applied AI Systems Engineer who will work closely with the founder to turn ambiguous product problems into reliable AI systems.

We need someone who can sit between product thinking, agent architecture, experimentation, and implementation. You will take an ambiguous problem such as:

"Disha sometimes forgets important details or uses outdated information when moving between chat and voice conversations."

...and convert it into a concrete system design:

"We need a structured memory layer, rules for what gets written and retrieved, conflict-resolution and expiry logic, context assembly across channels, evaluation datasets for memory quality, and production monitoring."

You will then turn that design into an implementation plan, prove it out through prototypes and internal tooling—vibe coding is encouraged—study where it fails, and iterate until the design is solid. Production systems are built by the tech team; your job is to hand them a design that has already been validated. You will not simply receive detailed specifications—you will help create them.