Senior Machine Learning Engineer (Computer Vision)

Location: Remote / Serbia

Company

hirify.global is developing an AI-driven digital pathology project combining machine learning, computer vision, and biomedical imaging.

What you will do

• Design, develop, and optimize machine learning and computer vision models for biomedical and digital pathology imaging.
• Build scalable pipelines for large-scale, high-resolution imaging datasets, including preprocessing, augmentation, training, validation, and evaluation.
• Develop and train deep learning models with Python and PyTorch.
• Run experiments, evaluate performance, and apply cross-validation, hyperparameter tuning, and model optimization.
• Deploy models into production with a focus on scalability, reliability, performance, maintainability, and reproducibility.
• Collaborate with AI researchers, biomedical experts, and software engineers to translate domain problems into ML solutions.

Requirements

• 5+ years of hands-on machine learning experience, including significant computer vision and deep learning experience.
• Bachelor’s, Master’s, or Ph.D. in computer science, electrical engineering, machine learning, data science, or a related technical field.
• Strong Python skills and hands-on experience with PyTorch or comparable frameworks such as TensorFlow.
• Knowledge of modern computer vision architectures, training techniques, model evaluation, optimization, and large-scale image-processing pipelines.
• Experience deploying ML models to production and working with AWS or another major cloud platform.
• Strong software engineering practices, including Git-based development, testing, code reviews, documentation, and collaborative workflows. Excellent written and spoken English is required.

Nice to have

• Experience with biomedical imaging, digital pathology, medical imaging, or healthcare ML applications.
• Experience with microscopy, whole-slide imaging, or other very large and high-resolution images.
• Familiarity with OpenCV, scikit-learn, Keras, Docker, or Kubernetes.
• Experience with MLOps, model monitoring, experiment tracking, reproducible pipelines, and deep learning performance optimization.
• Experience in research-intensive environments and translating experimental approaches into production-ready solutions.

Culture & Benefits

• Work with AI researchers, biomedical experts, and software engineers in a multidisciplinary distributed environment.
• Contribute across the full machine learning lifecycle, from experimentation and model development to production deployment.
• Address research-oriented technical challenges in computer vision, deep learning, and biomedical imaging.