Purpose

The goal of the Machine Learning Engineer is to introduce new solutions based on machine learning, especially deep learning, to improve accuracy and efficiency of data analysis from data preparation, model training and testing to deployment in a productive environment.

Responsibilities

• Applies supervised techniques, mostly deep learning techniques, for solving pattern recognition problems in ultrasound data (images, signals)
• Understands and interprets data to come up with optimal feature sets
• Prepares datasets to train and validate models
• Ensures quality of the developed prototypes by software testing and code reviews
• Writes documentation of developed solutions and ensures knowledge transfer

Requirements

• Bachelor’s or master's degree in computer science or a related field (PhD preferred)
• Relevant work experience more than 1 year, in machine learning, deep learning or similar
• Experience working on end-to-end ML projects, from data collection to production-level deployment
• Experience with image recognition, object detection, and other computer vision tasks.
• Experience with PyTorch is preferable; PyTorch lightning, loggers (W&B, neptune.ai...) a plus
• Experiment tracking and reproducibility (hydra)
• Experience training model in the cloud (ideally with AzureML)
• Be able to read SQL databases
• Familiarity with big data tools like Apache Spark for data manipulation is a plus.
• Experience with git, git workflows, or contribution on git hub is preferred
• Must be proficient in spoken and written English

Skillset

• Analytical mindset – Ability to break down complex problems and approach them with structured thinking
• Curiosity & innovation – Passion for exploring new technologies and pushing boundaries in machine learning
• Collaboration – Comfortable working in cross-functional, international teams and sharing knowledge openly
• Clear communication – Able to explain technical concepts to both technical and non-technical stakeholders
• Adaptability – Willingness to learn, iterate, and adapt to evolving tools, data, and business needs
• Detail-oriented – Strong focus on accuracy, quality, and reliability in both code and results
• Time management – Capable of managing multiple tasks and priorities in a flexible work environment
• Cultural awareness – Appreciation for diverse perspectives in a global team setting
• Self-driven learning – Proactive in staying up to date with the latest trends in ML and AI