ML Engineer (Computer Vision & ML Ops)

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About the role

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. 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 execute the ML operations pipeline — data gathering, annotation, preparation, training, testing and deployment • Propose a data collection strategy — research suitable public and clinical data sources, recommend patient consent and other administrative requirements for ML training (e.g., data protection agreements, ethics approval), and define collection and annotation protocols with clinical experts, including labeling guidelines and quality checks • Identify and validate factors that affect model performance — image conditions, dataset bias, patient diversity, data privacy and regulatory requirements • Define test strategies and metrics, and validate model outputs against clinician assessments • Perform build vs. buy analyses for models, tools and services, including cost comparison • Estimate compute and training costs and propose budgets for model training and deployment • Choose and set up training environments flexibly — local hardware or cloud — based on cost, data privacy and performance • Make pragmatic technical decisions appropriate for the current prototyping stage and document results clearly • Maintain clear, traceable documentation of data sources, models, experiments and design decisions to support audits and quality reviews • Evaluate and integrate additional sensors where feasible (e.g., depth, LiDAR, thermal, multispectral) and fuse them with RGB inpu

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