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

For our patients, the app is the entire product.

There’s no dashboard to fall back on, no rep to walk them through onboarding, and no second chance if a capture flow confuses them. If the app is hard to use, the patient stops using it and the care stops with it.

That’s why mobile is one of the most consequential surfaces at Deya, and the one we’re most deliberate about.

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.

Every one of those images and every symptom report comes through the app. The phone isn’t a companion to the product; it’s the instrument.

The role

We’re looking for a mobile engineer to own the Deya patient app: architecture, camera and capture pipeline, performance, and the experience itself.

You’ll work in React Native, dropping into Swift and Kotlin where the native layer matters, and for camera work, feeding a clinical image pipeline.

This is a high ownership, high velocity role. You’ll work directly with our clinicians and with the core engineering team, and you’ll have real authority over how the patient experience works.

What you’ll work on

• The Deya patient app and doctor app: React Native, Swift and Kotlin
• Camera and image-capture flows that produce clinically usable ocular images from a handheld phone
• Guided capture: real-time feedback on framing, focus, and lighting, with retry logic that gets a good image quickly
• Longitudinal patient flows: symptom check-ins, treatment adherence, and reminders people actually complete
• Offline resilience, secure local handling of health data, and upload reliability on poor connections
• Accessibility and legibility for older patients and patients with impaired vision: a real constraint in eye care, not an afterthought
• Design and interaction decisions alongside the team, from wireframe to shipped detail
• Release, instrumentation, and crash/quality monitoring across iOS and Android

How we build

Claude is part of our daily development workflow, and we want someone who already builds this way, using it to move fast across React Native and two native platforms without letting quality slip. That means real judgment about where these tools help and where they don’t, and full ownership of reviewing and testing what comes out of them.

Who you are

• You’ve shipped real mobile apps that real people use, and supported them after launch
• Familiar with native hardware and software capabilities of the iPhone and Android devices.
• Strong in React Native, comfortable writing native Swift, and some Kotlin
• UX and UI-minded: you have taste, you have opinions about interaction, and you can argue for them
• You think about the person holding the phone: their age, their eyesight, their patience, their connection
• Excited and comfortable using AI tooling for development
• Bonus: camera/media pipelines, on-device ML, computer vision, or health app experience
• Fast, scrappy, and high ownership

Why this is a unique moment

• The patient surface in eye care is almost entirely unbuilt; there’s no incumbent experience to imitate
• Your design decisions directly determine the quality of a category-defining clinical dataset
• Clinician-led team: optometrists and ophthalmologists in-house to pressure-test what you build
• Real patients using it now, so feedback loops are days, not quarters

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
• A portfolio or app link: we want to see and use what you’ve shipped
• A few sentences on an app experience you think is well designed, and why

The dataset is the hard part. We already have it.

Most people working on AI in eye care are limited by data: small sets, clean lab captures, no outcomes attached, no way to see what happened to the patient next.

We have the largest dataset of eye images captured on a smartphone; it’s growing through active clinical pilots, and it’s tied to symptoms, treatments, and outcomes over time.

That’s the moat. This role is about turning it into clinical signal.

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

You’d be the technical owner of AI at Deya, across two connected halves.

Vision. Our computer vision pipeline for smartphone-captured ocular imaging already exists and is in use. You’d take it over and take it considerably further; more robust models, trained and validated against our own clinical data, with inference that holds up on images captured by patients in their kitchens rather than by technicians in a clinic.

Language. The longitudinal record is only useful if something can reason over it. You’d build the LLM and agentic systems that summarize a patient’s history for a clinician in seconds, surface changes worth a doctor’s attention, and guide patients through check-ins and treatment adherence, with the evaluation discipline a clinical context demands.

This is a high ownership, high velocity role. You’d set the technical direction for AI here, working alongside the core engineering team and directly with our clinicians.

What you’ll work on

• Taking ownership of our existing ocular image pipeline and extending it
• Training, evaluating, and deploying vision models against our proprietary clinical data
• Ground truth definition, working directly with optometrists and ophthalmologists
• Handling the realities of consumer capture: variable lighting, focus, framing, skin tone, and device hardware
• LLM systems over longitudinal patient data retrieval, structured output, tool use, and agentic workflows for both patients and clinicians
• Serious evaluation infrastructure: eval sets, regression testing, failure analysis, and knowing when a model shouldn’t be trusted
• Guardrails, human review paths, and honest calibration of what the system does and doesn’t claim clinically
• Model serving, inference cost and latency, and on-device vs. cloud tradeoffs

How we build

AI is part of our daily development workflow, and for this role that goes further than tooling. Model capability is a moving target, and we want someone who treats keeping current as part of the job: reading the work, running new models against our own evals, and telling us when something we built six months ago should be rebuilt or thrown out. Bring your own view rather than taking published benchmarks at face value.

Who you are

• Production experience across computer vision and machine learning and shipped systems, not just notebooks and papers
• Hands-on with modern LLMs: prompting, evals, retrieval, tool use, fine-tuning where it’s warranted
• Strong engineering fundamentals; you can own your models in production rather than handing them off
• Comfortable working from an ambiguous clinical question to a defined ML problem
• Excited and comfortable using AI tooling for development
• Rigorous about evaluation and appropriately skeptical of your own results, which matters more when the output touches patient care
• Bonus: medical imaging, ophthalmic imaging, on-device ML, or clinical ML experience, EHR integrations
• Fast, scrappy, and high ownership

Why this is a unique moment

• A proprietary, growing clinical dataset most research groups simply can’t access, and you’d influence how it’s collected
• Vision + LLMs + longitudinal outcomes in a single system is genuinely underbuilt
• Clinicians in-house to define ground truth and validate what you build
• Because we control capture, you can improve the data itself, not just model around its limits
• Eye care is a massive, overlooked wedge in healthcare where AI hasn’t been meaningfully applied

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
• A link to your work, papers, repos, models, or shipped systems (add any others in your note)
• A few sentences on a model or LLM system you built and what you learned when it failed