Entwickelt und verantwortet die iOS-App, die ein iPhone am Mikroskop mit On-Device-Computer-Vision-Modellen in Echtzeit betreibt.
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Warum dieser Job gelistet ist
Kernaufgabe ist iOS-Entwicklung mit benannten On-Device-ML-Anforderungen (Core ML, Vision, Quantisierung, Neural Engine) für Echtzeit-Bildverarbeitung, die Modelle selbst werden aber nicht gebaut.. Prüfverfahren ansehen
We re looking for an iOS engineer who loves the camera. Our app turns an iPhone on a microscope into a lab instrument, and the hard problems live on the device: holding focus on a moving sample, running computer vision in real time, and capturing a recording precise enough for a clinical result. On top of that, the app has to be simple enough for a technician in a busy lab. You ll own it end to end, together with the founders.
Own the app end to end, from architecture to release. Push the iPhone camera and on-device models to their limits, so every recording is good enough to analyse. Design the capture workflow with lab staff, so technicians get it right the first time. Spend time in the lab, measure what works, and decide what to build next.
Camera depth. You ve built on the iOS camera stack, not just used it: AVFoundation with manual focus, exposure and white balance, and real-time frame processing. You know where the hardware s limits are.
On-device ML. You ve run models on the phone and made them fast: Core ML and Vision, quantisation, the Neural Engine, profiling with Instruments. Apps you ve owned.
You ve shipped iOS apps in Swift and SwiftUI and owned them in production. You design an architecture one engineer can carry, and you can explain the trade-offs behind it. Product and UX sense.
You turn a messy real-world workflow into something simple, and you test it with the people who use it. Built for real conditions. Offline-first, background uploads, interruptions, data protection.
The app works when the network doesn t. Beyond the app. iOS is your home, but when the app needs it, you read the backend and change an API yourself. Correctness.
You validate with numbers, not by eye. Tests are part of the feature. You reproduce bugs first and fix the cause, not the symptom.
AI-native engineering. Coding agents are part of your daily work. You give them good context, split work into reviewable steps, and stand behind every line you ship.
Ownership. You help shape the roadmap, push back on weak ideas, set your own priorities and raise problems early. Nobody has to chase you.
Range. You ve worked across several companies, stacks or environments. How you work counts far more than how many years you ve done it.
Optics or microscopy imaging. Apps for professional users: lab, field, clinical or enterprise. Regulated environments with audits: medical devices, health, finance.
Ownership from day one. As a founding engineer, you re part of the company through our virtual share option plan (VSOP). You grow with us. As the company grows, your role grows with it. Real influence. You decide how we build software, and you shape the engineering team we build. A competitive salary. A product that matters: a medical device for real clinics, built together with clinicians and lab staff. Flexible hours and office days. We work from Berlin and meet in person regularly, up to several days a week. This isn t a fully remote role. English is our working language. German isn t required. We sponsor the EU Blue Card if you re moving to Berlin.
Most labs still count cells under a microscope by hand. It s slow, repetitive, and error-prone. We re replacing that manual work with software.
By combining a standard microscope with a smartphone, we run computer vision models that locate, track, and measure cells automatically in real time. We re starting with semen analysis and expanding to other routine microscopic samples. We re a small, well-funded team in Berlin, founded in 2026.
Our goal is to make automated, reliable diagnostic analysis accessible on any phone connected to a microscope.
Interesse an dieser Position?
Die vollständige Anzeige und das Bewerbungsformular liegen beim Arbeitgeber.
Zur Originalanzeige82 geprüfte Stellen bei 56 Arbeitgebern, 4 davon ortsunabhängig. Modelle bauen, trainieren und verbessern: von klassischem Machine Learning über Computer Vision bis zum Feinschliff großer Sprachmodelle.