Most AI in healthcare is being built in the wrong place.
Everyone is chasing scribes and diagnostics. Almost no one is building the longitudinal data layer that captures what actually happens to patients over time.
That’s the real moat.
We’re building that layer for eye care.
What we’re building
Deya is an AI platform that sits between doctor and patient, and captures symptoms, treatments, outcomes, and images over time to deliver remote patient care.
We already have the largest dataset of eye images captured on a smartphone and are currently piloting our platform with eye doctors and collecting real patient data.
The goal: build the largest longitudinal dataset in eye care (behavior + outcomes + ocular images) captured from connected devices, and use it to power AI systems that extend and improve clinical eyecare.
The role
We’re looking for a software engineer to help build this from the ground up.
You will help define architecture, product, and AI systems from day one.
This is a high ownership, high velocity role you’ll work alongside the core team to shape the technical foundation of the company.
What you’ll work on
- Full-stack product development (Next.js, backend services, cloud infra)
- Agentic workflows for patients and clinicians
- Computer vision pipelines for ocular imaging
- Infrastructure for longitudinal clinical datasets
- Core architecture decisions across the stack
How we build
AI is part of our daily development workflow: writing and reviewing code, exploring unfamiliar systems, and moving quickly across a broad stack with a small team. We expect you to already work this way, to have real opinions about where these tools earn their keep and where they get in the way, and to be someone who takes full responsibility for reviewing and testing what comes out of them.
Who you are
- Strong builder: you’ve shipped real products end-to-end
- Comfortable across the stack (frontend, backend, infra)
- Experience building production AI/ML systemsExcited and comfortable using AI tooling for development.
- Fast, scrappy, and high ownership
- Bonus: experience with healthcare technology and EHR integrations
Why this is a unique moment
- Eye care is a massive, overlooked wedge in healthcare
- Data is fragmented, workflows are broken, and AI hasn’t been meaningfully applied
- We’re in a position to capture a category-defining dataset early
- The combination of longitudinal data + vision + LLMs is extremely underbuilt
This is not incremental. It’s a chance to help define the layer everything else will sit on.
Details
- Full-time
- NYC (hybrid)
- Competitive salary + equity
If this sounds interesting, please apply!
- Resume
- Portfolio
- A few sentences on why this problem
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