ML Engineer (Computer Vision & ML Ops)

StellDirVor GmbH; Deutschland; On-site; EUR 90,000 - 130,000; Full time

Job summary

StellDirVor GmbH is seeking a ML Engineer (Computer Vision & ML Ops) to build AI capabilities for a hands-free training and assistance system in healthcare. You will own the full data lifecycle from sourcing to deployment, and work within a small team on a prototype-oriented project.

The ideal candidate has a solid ML foundation, strong Python skills, and experience with PyTorch, OpenCV or TensorFlow, plus knowledge of medical imaging challenges and privacy considerations.

Qualifications

• Must have degree in a relevant field and solid ML foundation.
• Strong Python skills and hands-on use of PyTorch, OpenCV or TensorFlow.
• Solid understanding of computer vision - classification, detection and segmentation; 3D reconstruction is a plus.

Responsibilities

• Select and benchmark pre-trained models for medical image analysis and adapt to domain data.
• Identify generative AI models to create realistic synthetic medical images for education and training.
• Evaluate and fine-tune language models for voice interaction, low-latency on-device use with smart glasses.
• Plan and execute the ML operations pipeline: data gathering, annotation, preparation, training, testing and deployment.
• Define data collection and annotation protocols with clinical experts, including labeling guidelines and quality checks.

Skills

Python

PyTorch

OpenCV

TensorFlow

Computer Vision

Transfer Learning

Education

Degree in Computer Science / Data Science / Biomedical Engineering

Job description

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 input

Requirements

• Must-Have Degree in Computer Science, Data Science, Biomedical Engineering, Mathematics or a related field - or equivalent practical background
• Strong Python skills and hands-on use of PyTorch, OpenCV or TensorFlow
• Solid understanding of computer vision - classification, object detection and image segmentation, 3D Reconstruction and Multi-View Geometry
• Hands-on knowledge of transfer learning - adapting pre-trained models and fine-tuning them on custom datasets
• Working knowledge of the full ML lifecycle - data preparation, training, evaluation and deployment
• Basic knowledge of generative models (diffusion models or GANs) and common open-source libraries
• Comfortable training models both locally (GPU workstation) and on cloud platforms
• Able to estimate ML compute costs and compare build vs. buy options to support budget decisions
• Awareness of medical imaging challenges - limited data, label variability between clinicians and class imbalance
• Awareness of healthcare data privacy and ethics (e.g., GDPR, patient consent, anonymization) and why explainability and bias matter in clinical AI
• Able to read research papers and apply them