Start: Estimated Summer/Autumn 2026
Location: Primarily remote, with occasional in-person sessions in Helsinki, Finland.
Language: English

Position: Machine Learning / Computer Vision Engineer – Romania (Fully Remote)

We’re partnering with a healthcare innovator at the forefront of AI-driven clinical solutions – spanning diagnostics and predictive modeling – and we’re looking for an Imaging Specialist who thrives at the intersection of medical imaging and machine learning.

In this role, you’ll design and optimize advanced algorithms powered by DICOM-based data to support real-world clinical decision-making. You’ll work with complex imaging modalities such as CT, MRI, PET, ultrasound, and digital pathology, leveraging state-of-the-art frameworks including PyTorch, TensorFlow, MONAI, and nnU-Net. Your work will directly contribute to high-impact healthcare applications, with a strong focus on model robustness, validation, and regulatory alignment.

What You’ll Bring

DICOM Expertise
Hands-on experience (at least one year since Jan 2021) working with DICOM images and metadata – processing, interpreting, and pseudonymizing data. Strong understanding of DICOM structures (headers, tags, modalities) and integration with systems like PACS in ML pipelines.

Proven Delivery
Track record of delivering algorithms using DICOM images as input – demonstrated through a completed project or a published scientific paper.

Machine Learning in Medical Imaging
Minimum one year of experience building and training models on medical imaging data. Solid exposure to segmentation, classification, and/or registration tasks using frameworks such as PyTorch, TensorFlow, MONAI, nnU-Net. Experience spans both 2D and 3D datasets (CT, MRI, PET, ultrasound, digital pathology), including multimodal and multi-resolution data processing and normalization.

Validation & Performance Evaluation
Experience validating ML models in medical imaging contexts – minimum 6 months. Proficient with metrics like AUC, Dice, IoU, Sensitivity, Specificity, and experienced in model benchmarking and automated testing using tools such as Pytest, MLflow, MONAI Eval. Practical understanding of model generalization, data drift, and bias assessment.

If you’re motivated by building impactful AI solutions in healthcare and want to work with cutting-edge imaging technologies in a flexible, remote-first setup, this is a strong opportunity to make a difference.