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.

• Lead real, complex technical challenges: work on a large, complex codebase where analytics and modeling capabilities are central. Handle significant traffic (2.3M registered users, 200K unique monthly visitors) and large volumes of data. Strong emphasis on clean architecture to support long-term growth. • Work on an attractive technical stack: We use MongoDB, Node.js and Vue.js — three of the most popular JavaScript technologies on the market. We are currently migrating to TypeScript. • Grow within an R&D team that enables rapid progress: our decentralized peer-review process provides regular, qualitative feedback from colleagues. We promote pair programming and knowledge sharing.

• Work closely with the founder to design and build an AI-powered content generation system from the ground up • Contribute to meaningful parts of the product end-to-end from how the system ingests and understands source material, to how it produces and validates outputs, to how instructors interact with and review what the system generates • Build and iterate on LLM-driven pipelines • Work with retrieval and embedding techniques to ground outputs in real source material • Develop backend services and APIs that tie everything together • Think about output quality and building evaluation steps, catching failure modes, and improving the system based on real instructor feedback • Research new tools and techniques as the AI space evolves and bring relevant ideas directly into the product • This is a generalist role at an early-stage product where you'll wear multiple hats, work with ambiguity, and have direct input into how things are built.

• Research, design, develop, and implement advanced machine learning (ML) solutions • Ensure deployed models are scalable, reliable, and provide tangible value • Develop models to predict future trends, user behavior, and business outcomes • Create systems to categorize data, such as sentiment analysis of customer feedback or document classification • Build and fine-tune models capable of generating new content • Work with architectures like Transformers and Large Language Models • Perform data cleaning, preprocessing, and exploratory data analysis • Collaborate with various engineering teams to integrate AI solutions into products and platforms • Maintain a robust and performant 'feature store' • Utilize Infrastructure as Code (IaC) tools for deploying ML models into cloud-based environments • Incorporate services like speech-to-text and integrate NLP and GenAI capabilities • Prepare detailed reports and presentations on ML experiments • Provide guidance and mentorship to junior engineers