Role Description We are currently developing ARAIAS — an AR- and AI-based hands-free training and assistance system for chronic wound care. The system enables 3D wound capture, AI-supported analysis, and standardized remote expertise and documentation directly at the point of care. - Design and build AI models for wound analysis — from architecture decisions through evaluation and iteration, with a focus on getting to reliable, clinically meaningful outputs. - Apply computer vision techniques — object detection, segmentation, depth estimation, or 3D reconstruction — to real medical imaging challenges. - Handle pre-processing and post-processing of multi-sensor imaging data — including 2D images, depth maps, and other sensor inputs. - Design and build REST APIs and cloud infrastructure that connect and support all system components. - Prototype hardware-software integration — interfacing with smartglass SDKs and sensor APIs to establish data capture pipelines from device to back-end. - Integrate AI models into application layers — inference endpoints, model serving, versioning, and performance monitoring. - Make pragmatic architectural decisions appropriate for the current prototyping stage. - Set up CI/CD, containerization, and basic observability to keep the team moving fast. - Support integration points between the back-end / AI layer and the Unity-based front-end. Qualifications - Hands-on experience with computer vision — image segmentation, object detection, and classification. - Experience designing and adapting model architectures — going beyond basic fine-tuning to make informed decisions about model structure, loss functions, and training strategies. - Ability to evaluate model outputs critically — not just metrics, but understanding what the results mean in context. - Back-end engineering proficiency — REST API design with Python as the primary language. - Cloud platform experience (AWS / GCP / Azure). - Comfort working with hardware SDKs and APIs, and able to independently navigate technical documentation and integration guides to establish device-to-server data flows. - Comfortable with Docker and basic CI/CD pipelines. - Familiarity with ML tooling: PyTorch or TensorFlow. - Fluent in English, German is a bonus. - Ability to take initiative, own problems end-to-end, and make pragmatic technical decisions independently. Requirements - Database experience — PostgreSQL, NoSQL, object storage (S3 / GCS). - Unity experience or familiarity with Unity's integration patterns. - Familiarity with semi-supervised learning — leveraging limited or partially labeled data, relevant when annotated wound imaging data is scarce. - Experience with 3D data pre-processing and post-processing — depth map handling, point cloud cleaning, mesh reconstruction, or similar. - Familiarity with hardware SDK integration — experience interfacing with smart glasses, depth sensors, or similar hardware to extract and stream sensor data. - Exposure to multi-modal learning — combining RGB and depth or LiDAR data into unified model inputs. Benefits - Meaningful work — You help shape how technology redefines education and training in healthcare, with real impact on clinical outcomes. - Ownership and creative freedom — You work autonomously, contribute your ideas directly, and have a genuine say in how the system is built. - A learning culture — We support personal and professional growth through knowledge sharing, access to innovative tools and methods, and a dedicated learning budget. - Flexible working — Remote-friendly setup with the option to work from our Munich office. - Flat hierarchies and team spirit — A small, open, and trust-based team where communication is direct and everyone's voice matters. - International collaboration — Work alongside a Taiwan-based partner covering technology, hardware, and research.