Senior Machine Learning Engineer

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.  Tasks Applies supervised techniques, mostly deep learning techniques, for solving pattern recognition problems in ultrasound data (images, signals) AI model training and research Take ownership of end to end projects from proposing technical solutions, model development, technical project management, results communication to stakeholders, until handover the solution to the production team Mentoring and support our Machine Learning junior members Understands and interprets data to come up with optimal feature sets or images Prepares datasets to train and validate models Ensures quality of the developed prototypes by following good software practices 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 5 years, in deep learning (preferred image related) 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. Must be proficient in spoken and written English Proficiency in  Python  PyTorch/TensorFlow/Jax Experiment tracking (ex: W&B, MLFLow or similar) Nice to have Be able to read SQL databases 2 years of experience with classical ML Experience with big data tools like Apache Spark for data manipulation is a plus. Cloud experience like: Azure ML, AWS sagemaker or Vertex AI 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