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If the qualifications below match your background and the role aligns with your goals, please send your Job Application Form to info@lbsoftist.com.

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AI Software Engineer

We are looking for a teammate who will build solutions with real impact in healthcare technology, focusing on computer vision and medical imaging for the OncoEASE project.

Position Details

Job Title: AI Software Engineer (Computer Vision and Medical Imaging)
Project: OncoEASE (Oncological Enhanced Analysis and Intelligent Evaluation)
Location: Bilkent Center Hane TEKMER, Ankara, Türkiye
Employment Type: Full-time

About the Role

Are you excited by the idea of using artificial intelligence to solve real-world healthcare problems? We are looking for a talented Software Engineer with a strong focus on machine learning and computer vision to join the OncoEASE project.

In this role, you will develop advanced deep-learning models that analyze medical images and contribute to the early detection and diagnosis of oncological conditions in the DiagnoSENSE module.
You will bridge complex algorithms and clinical practice, helping ensure our software is accurate, explainable, and safe in treatment-planning decision support through the TreatMIND module.

Key Responsibilities

Design and implement deep-learning models—particularly CNN-based approaches—for medical-image classification and segmentation.
Build end-to-end medical-image processing workflows, from reading DICOM files and extracting ROIs to normalization and data augmentation.
Apply transfer-learning techniques to adapt current architectures, including ImageNet-pretrained models, to medical datasets.
Collaborate with the team to integrate AI models into the OncoEASE software pipeline.
Ensure all data-processing workflows comply with medical-data privacy standards under KVKK/GDPR.

Required Technical Qualifications

1. Machine Learning and Deep Learning

Strong command of CNN architectures such as ResNet, EfficientNet, and DenseNet.
Experience using transfer learning to adapt general computer-vision models to medical data.
Proficiency in Python and the machine-learning ecosystem: NumPy, Pandas, and scikit-learn.
Hands-on experience with deep-learning frameworks: PyTorch preferred, plus TensorFlow.

2. Medical Image Processing

Experience with medical-imaging modalities such as MRI, PET-CT, mammography, or ultrasound.
In-depth knowledge of the DICOM standard and file structure.
Ability to implement essential preprocessing steps such as ROI extraction, image normalization/standardization, and artifact removal.
Expertise in data-augmentation techniques specific to medical datasets.
Proficiency in image-processing libraries such as OpenCV or scikit-image.

3. Data Privacy and Ethics

Awareness of KVKK/GDPR regulations concerning sensitive personal data.
An understanding of ethical responsibilities in processing healthcare data and a work approach aligned with those responsibilities.

Preferred Qualifications

Explainable AI (XAI): familiarity with interpretability tools such as Grad-CAM, SHAP, or LIME for visualizing and validating model decisions for clinicians.
Experience deploying machine-learning models to production environments.