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.TasksSelect 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 conditionsIdentify and adapt suitable generative AI models to create realistic synthetic medical images for education and trainingEvaluate 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 glassesResearch 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 deploymentPropose 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 checksIdentify and validate factors that affect model performance — image conditions, dataset bias, patient diversity, data privacy and regulatory requirementsDefine test strategies and metrics, and validate model outputs against clinician assessmentsPerform build vs. buy analyses for models, tools and services, including cost comparisonEstimate compute and training costs and propose budgets for model training and deploymentChoose and set up training environments flexibly — local hardware or cloud — based on cost, data privacy and performanceMake pragmatic technical decisions appropriate for the current prototyping stage and document results clearlyMaintain clear, traceable documentation of data sources, models, experiments and design decisions to support audits and quality reviewsEvaluate and integrate additional sensors where feasible (e.g., depth, LiDAR, thermal, multispectral) and fuse them with RGB inputRequirementsMust-HaveDegree in Computer Science, Data Science, Biomedical Engineering, Mathematics or a related field — or equivalent practical backgroundStrong Python skills and hands-on use of PyTorch, OpenCV or TensorFlowSolid understanding of computer vision — classification, object detection and image segmentation, 3D Reconstruction and Multi-View GeometryHands-on knowledge of transfer learning — adapting pre-trained models and fine-tuning them on custom datasetsWorking knowledge of the full ML lifecycle — data preparation, training, evaluation and deploymentBasic knowledge of generative models (diffusion models or GANs) and common open-source librariesComfortable training models both locally (GPU workstation) and on cloud platformsAble to estimate ML compute costs and compare build vs. buy options to support budget decisionsAwareness of medical imaging challenges — limited data, label variability between clinicians and class imbalanceAwareness of healthcare data privacy and ethics (e.g., GDPR, patient consent, anonymization) and why explainability and bias matter in clinical AIAble to read research papers and apply them pragmaticallySelf-directed and pragmatic — able to take initiative and own problems end-to-end in a lean teamFluent in English — German is a bonusStrong PlusProjects or coursework in medical imaging or clinical AIFamiliarity with semi-supervised learning — leveraging limited or partially labeled dataExperience with 3D data processing — depth maps, point clouds or mesh reconstructionExposure to multi-modal learning — combining RGB and depth or LiDAR dataEdge AI or wearable deployment (e.g., TensorFlow Lite, ONNX, model quantization)Knowledge of explainability methods (e.g., Grad-CAM, SHAP) or adversarial robustnessFamiliarity with MLOps tools (e.g., MLflow, Weights & Biases, DVC, Docker)Awareness of medical device regulation for AI software (e.g., EU MDR, IEC 62304)Familiarity with small language models (SLMs) and speech pipelines (e.g., Whisper, on-device speech recognition and text-to-speech)BenefitsMeaningful work — You help shape how technology redefines education and training in healthcare, with real impact on clinical outcomesOwnership and creative freedom — You work autonomously, contribute your ideas directly, and have a genuine say in how the system is builtA learning culture — We support personal and professional growth through knowledge sharing, access to innovative tools and methods, and a dedicated learning budgetFlexible working — Remote-friendly setup with the option to work from our Munich officeFlat hierarchies and team spirit — A small, open, and trust-based team where communication is direct and everyone's voice mattersInternational collaboration — Work alongside a Taiwan-based partner covering technology, hardware and researchWe are only able to respond to candidates selected for further consideration. If you have not heard from us within four weeks of applying, please consider your application unsuccessful at this time. Find Jobs in Germany on Arbeitnow