ML Engineer (Computer Vision & ML Ops)

StellDirVor is an independent consulting, trade and technology company specializing in immersive technologies for healthcare. We support organizations in implementing digital innovations — Virtual, Augmented and Mixed Reality as well as AI — particularly for learning, training and real-time assistance in clinical and care environments. By combining technology and healthcare, we help optimize processes, improve knowledge transfer and enhance quality of care. Supported by a Germany (ZIM) and Taiwanese (GIPIP) partnership grant, we are currently developing ARAIAS — an AR- and AI-based hands-free training and assistance system for chronic wound care. Using AR smart glasses, the system enables 3D capture, AI-supported analysis, and standardized remote expertise and documentation directly at the point of care. As our ML Engineer (Computer Vision & ML Ops), you will build the AI capabilities at the core of ARAIAS: evaluating and fine-tuning computer vision models on smart glasses data, running the ML operations behind them, and applying generative AI to create synthetic medical images for education. We're at an early stage — moving from concept to working prototype — so the near-term focus is on validating models and building toward a proof of concept we can test in real education and clinical settings. As part of a small team, you will own the full data lifecycle end to end — from sourcing, consent and annotation to preparation — alongside model development and deployment. We welcome candidates at different experience levels. What matters most is a solid ML foundation and genuine awareness of the medical imaging domain. Notes: Due to the nature of the project, we can only consider candidates who are already residing in Germany. Tasks Select and benchmark pre-trained models and/or validated architectures for medical image analysis and adapt them to domain-specific data, ensuring robust performance under real-world conditions Identify and adapt suitable generative AI models to create realistic synthetic medical images for education and training Evaluate and fine-tune small language models (SLMs) for voice interaction — speech-to-text, intent understanding and text-to-speech — optimized for low-latency or on-device use with smart glasses Research and assess state-of-the-art ML models and emerging trends, including Explainable AI (XAI), cybersecurity for ML, and deployment and distribution modes (edge vs. cloud, federated learning, model compression) Plan and...

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