Senior Fullstack Engineer — Mobile Healthcare Platform

Snke·Münchensenior

In short

→Ingeniero fullstack senior que trabaja en una plataforma móvil de salud clínica en desarrollo constante.
→Cambia de stack diariamente: móvil (iOS/Android), backend, integraciones con hospitales y despliegue en cloud o on-premise.
→El trabajo está impulsado por IA agente: especifiques tareas, revisas código generado y mantienes la calidad del sistema.

Professional English communication skills; German is a strong advantage

✓ Free to start✓ Runs in your browser✓ First dossier, no card✓ Ready in ~1 minute

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The questions they'll ask you

1. ¿Cómo especificarías una tarea para un agente de IA para implementar una función de sincronización offline en una app móvil?

🔒 +7 more questions

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💵 USD · Remote · No visa

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What they ask for

✓Experiencia profesional en producción con frontend/móvil y backend.
✓Toma de decisiones arquitectónicas sólidas con claridad en contratos y límites de módulos.
✓Fluidez con herramientas de desarrollo agente (ej. Claude Code, Cursor, Copilot).
✓Capacidad para especificar y revisar código generado por IA con rigor.
✓Experiencia con Android o Flutter, incluyendo ciclo de vida y comportamiento offline.
✓Forma estructurada y enfocada en calidad en entornos regulados.

Don't tick every box? That's normal — your free dossier shows your gaps and how to cover them in the interview.

PythonGoNETRustiOSAndroidFlutterDockerCI/CDFHIR

Who should you write to at Snke?

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What you'll do We are a startup building a platform that is in daily use during patient care, and our direction changes as we learn. The engineer we are looking for is comfortable with that: someone who moves between backend, mobile, integration and platform work as the product demands, and who picks up new technologies quickly because their strength is in engineering fundamentals rather than any one stack. Right now that means, among other things, bringing our clinical mobile app to Android, evolving how our product integrates with hospital systems, and keeping our services dependable across cloud and on-premise deployments. Six months from now the list will look different, and you will help decide what is on it. We build AI-native: agents write a large share of our code, and the engineer's job is to design the system, specify the work precisely, and keep the codebase in a state where both humans and AI tools can understand and extend it safely. Your engineering judgement is the product here. Move across the stack as priorities shift: mobile UI, backend services, APIs, hospital integrations, platform and deployment work Own the architecture of the parts you touch: module boundaries, API contracts, data models, and the decisions behind them Direct AI coding agents to implement against your specifications, and review their output with the same rigour you would apply to a colleague's pull request Keep the codebase legible for readers without context: consistent conventions, small well-named units, documented rationale, tests that express intent Own service quality end to end: automated testing, CI/CD, observability, diagnosability in production Work with clinical data and healthcare interoperability standards Produce the technical documentation and evidence required in a regulated development process What we're looking for Several years of professional experience building and operating software in production, spanning both frontend or mobile and backend work, and a track record of changing roles or stacks when the product needed it Strong grasp of software architecture: you decompose problems into modules with clear contracts, think about dependency direction and ownership of state, and can explain why a boundary sits where it does Fluency with agentic AI development tools (for example Claude Code, Cursor, Copilot agents). You can describe a concrete workflow you use, where it breaks down, and which guardrails you put in place. You know which code you would never hand to a model unreviewed A specification and review mindset: you define "done" before starting, treat generated code as a hypothesis to verify, and notice missing edge cases and error paths Grounding in fundamentals that transfer across stacks: concurrency, data consistency, networking, API design, relational data. You have switched languages before and can judge whether unfamiliar code is idiomatic.

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